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Oct 12, 2022 · Artificial Intelligence

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

This article presents a comprehensive overview of feature embedding modeling in recommendation systems, discussing the necessity of feature modeling, three technical directions (gate threshold, variable‑length embeddings, and enrichment), detailed descriptions of models such as FiBiNet, FiBiNet++, ContextNet, and MaskNet, experimental findings, and a Q&A session that addresses practical challenges and future work.

CTR modelsRecommendation SystemsWeibo

0 likes · 34 min read

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

21CTO

Sep 26, 2022 · Artificial Intelligence

Unlocking Live-Streaming Recommendations: Strategies from Tencent Music’s Interactive Systems

This article explores the evolution of recommendation systems for interactive live‑streaming scenarios, covering common system traits, user cold‑start solutions, prior knowledge modeling, scene‑specific modeling, and practical Q&A insights drawn from Tencent Music’s real‑world deployments.

Model OptimizationRecommendation Systemsai

0 likes · 19 min read

Unlocking Live-Streaming Recommendations: Strategies from Tencent Music’s Interactive Systems

Alimama Tech

Sep 21, 2022 · Artificial Intelligence

Alibaba's Three Papers Accepted at NeurIPS 2022

Alibaba’s research team secured three NeurIPS 2022 papers—introducing an Adaptive Parameter Generation network that boosts click‑through rates and revenue, a tuning‑free Global Batch Gradient Aggregation method that speeds recommendation model training by 2.4×, and a Sustainable Online Reinforcement Learning framework that outperforms existing auto‑bidding strategies.

NeurIPSRecommendation SystemsReinforcement Learning

0 likes · 6 min read

Alibaba's Three Papers Accepted at NeurIPS 2022

DataFunTalk

Sep 19, 2022 · Artificial Intelligence

Pretraining Models and Graph Neural Networks for Recommendation Systems

This talk explores the evolution, objectives, and core challenges of pretraining models, their application in recommendation scenarios, service modes, and detailed case studies of graph neural network pretraining, illustrating how self‑supervised learning and multi‑domain data integration enhance user and item embeddings for improved recommendation performance.

Multi-domainRecommendation Systemsgraph neural networks

0 likes · 16 min read

Pretraining Models and Graph Neural Networks for Recommendation Systems

ITPUB

Sep 15, 2022 · Artificial Intelligence

Why Precise Feature Engineering Still Matters in Recommendation Systems

In the era of deep learning, feature engineering remains crucial for recommendation and search advertising because it bridges raw relational data and models, improves performance, reduces complexity, and handles high‑cardinality, large‑scale, and time‑sensitive scenarios with robust transformations and statistical encoding.

Recommendation Systemsaidata preprocessing

0 likes · 20 min read

Why Precise Feature Engineering Still Matters in Recommendation Systems

DataFunTalk

Sep 10, 2022 · Artificial Intelligence

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

This article reviews how graph neural networks are applied across the three stages of recommendation systems—recall, ranking, and re‑ranking—detailing novel models such as NIA‑GCN, GraphSAIL, and DGENN, their experimental improvements, and future research directions.

GNN recallIncremental LearningRecommendation Systems

0 likes · 17 min read

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

Meituan Technology Team

Sep 8, 2022 · Artificial Intelligence

Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction

The article details Meituan Waimai's use of graph neural network techniques—feature‑graph crossing, subgraph expansion, and metapath‑based scene graphs—to model user‑restaurant interactions across location, time, and context, describing the engineering pipeline, online serving optimizations, and offline AUC improvements of up to 2.5 ‰ for high‑ and low‑frequency scenarios.

CTR predictionMeituan WaimaiRecommendation Systems

0 likes · 29 min read

Graph Neural Network Based Scene Modeling for Food Delivery CTR Prediction

DaTaobao Tech

Sep 7, 2022 · Artificial Intelligence

Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation

The team enhanced real‑time recommendation by redesigning TensorFlow graphs—using constant‑folding, a custom CallGraphOP cache, a simplified dense layer, and CUDA‑Graph compatibility—boosting single‑machine throughput ~40%, raising GPU utilization from 30% to 43%, cutting latency and saving roughly 30% of hardware resources.

CUDA GraphGPU performanceModel Optimization

0 likes · 11 min read

Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation

DaTaobao Tech

Aug 30, 2022 · Artificial Intelligence

CTNet: Continual Transfer Learning for Cross-Domain Recommendation

CTNet is a continual transfer learning framework that uses a lightweight Adapter to map source‑domain features onto evolving target‑domain recommendation tasks, preserving all model parameters to avoid catastrophic forgetting and delivering substantial gains in click‑through rate, conversion, and overall business performance in Taobao’s cross‑domain e‑commerce scenario.

Adapter ModuleRecommendation Systemscontinual learning

0 likes · 12 min read

CTNet: Continual Transfer Learning for Cross-Domain Recommendation

DataFunTalk

Aug 30, 2022 · Artificial Intelligence

Feature Engineering for Recommendation and Search Advertising

This article explains why meticulous feature engineering remains crucial in recommendation and search advertising, outlines what constitutes good features, describes common transformation techniques such as scaling, binning, and encoding, and provides practical examples and Q&A for practitioners.

Recommendation Systemsaidata preprocessing

0 likes · 18 min read

Feature Engineering for Recommendation and Search Advertising

Alimama Tech

Aug 24, 2022 · Artificial Intelligence

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

The authors introduce AGE, an adversarial‑gradient‑driven exploration framework that injects uncertainty‑scaled perturbations into ad embeddings to approximate the downstream learning effect, combines Monte‑Carlo dropout uncertainty, a dynamic gating unit, and achieves up to 15 % offline gains and 6 % online CTR improvement over strong baselines.

Exploration-ExploitationOnline LearningRecommendation Systems

0 likes · 14 min read

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

DataFunTalk

Aug 22, 2022 · Artificial Intelligence

Live‑Streaming Recommendation System: Interaction Scenarios, User Cold‑Start, Prior Modeling, and Scene Modeling

The article presents a comprehensive technical overview of a live‑streaming recommendation system, covering common and specific characteristics, user cold‑start strategies using unbiased clustering, prior knowledge integration, multi‑task modeling, and scene‑aware routing to improve relevance and engagement in interactive environments.

Recommendation Systemsclusteringfeature modeling

0 likes · 19 min read

Live‑Streaming Recommendation System: Interaction Scenarios, User Cold‑Start, Prior Modeling, and Scene Modeling

Hulu Beijing

Aug 19, 2022 · Artificial Intelligence

Disney’s M5 Model: Multi‑Modal, Multi‑Interest, Multi‑Scenario Boost for Streaming Recommendations

Disney’s Content Discovery team introduces M5, a multi‑modal, multi‑interest, multi‑scenario recall model that enhances VOD and live streaming recommendations by leveraging rich metadata, user behavior, and contextual features, outperforming baseline methods with significant hit‑ratio gains across Hulu and Disney+.

Deep LearningM5 modelRecommendation Systems

0 likes · 22 min read

Disney’s M5 Model: Multi‑Modal, Multi‑Interest, Multi‑Scenario Boost for Streaming Recommendations

WeChat Backend Team

Aug 5, 2022 · Artificial Intelligence

How WeChat’s Ekko Achieves Ultra‑Low‑Latency Model Updates for Billion‑User Recommendations

At the 16th OSDI conference, Tencent’s WeChat team presented the award‑winning Ekko system—a groundbreaking, ultra‑low‑latency model‑update solution for massive recommendation workloads that dramatically speeds up updates, supports over a trillion‑scale models, and has already boosted user engagement across billions of daily users.

Low latencyModel UpdateRecommendation Systems

0 likes · 5 min read

How WeChat’s Ekko Achieves Ultra‑Low‑Latency Model Updates for Billion‑User Recommendations

Meituan Technology Team

Jul 21, 2022 · Artificial Intelligence

Overview of Meituan Technical Team Papers Featured at ACM SIGIR 2022 and Related Works

The article highlights ten representative Meituan technical papers accepted at ACM SIGIR 2022, spanning personalized opinion tagging, cross‑domain sentiment classification, dialogue summarization transfer, universal retrieval, CTR prediction, image behavior modeling, and topic segmentation, each summarized with abstracts and download links for researchers.

Recommendation Systemscross-domain learninginformation retrieval

0 likes · 25 min read

Overview of Meituan Technical Team Papers Featured at ACM SIGIR 2022 and Related Works

vivo Internet Technology

Jul 20, 2022 · Artificial Intelligence

Collaborative Filtering and Matrix Factorization: Theory and Spark ALS Implementation

The article introduces collaborative filtering, derives the matrix‑factorization model R≈X·Yᵀ with L2‑regularized ALS updates, demonstrates a full Python example on a small rating matrix, then shows how to implement and scale Spark’s ALS for massive user‑item data, ending with production tips and references.

ALSRecommendation SystemsSpark

0 likes · 25 min read

Collaborative Filtering and Matrix Factorization: Theory and Spark ALS Implementation

DataFunSummit

Jul 11, 2022 · Artificial Intelligence

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

This article presents a comprehensive overview of conversion‑rate (CVR) optimization for Alitrip’s travel recommendation platform, detailing the challenges of extremely sparse user feedback, the design of item, user, query and context features, and a series of model‑level and loss‑function techniques—including generic‑label modeling, global‑transaction modeling, ESMM, rank‑loss approximations, and multi‑task CTR auxiliary training—to improve both CTR and CVR performance in high‑ticket‑price scenarios.

CVR optimizationRecommendation SystemsSparse Data

0 likes · 19 min read

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

DataFunTalk

Jul 8, 2022 · Artificial Intelligence

Tencent's Wuliang Deep Learning System for Large‑Scale Recommendation: Architecture, Challenges, and Solutions

This article presents an in‑depth overview of Tencent's Wuliang deep learning platform for recommendation systems, detailing the real‑time data challenges, high‑throughput requirements, parameter‑server architecture, model compression techniques, multi‑level caching, and answers to common technical questions.

Distributed TrainingInference ServiceParameter Server

0 likes · 14 min read

Tencent's Wuliang Deep Learning System for Large‑Scale Recommendation: Architecture, Challenges, and Solutions

DataFunSummit

Jul 6, 2022 · Artificial Intelligence

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

This article reviews several mature knowledge‑graph applications, describing Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Fourth Paradigm’s Sage Knowledge Base platform with various representation‑learning models, and additional use cases in recommendation, QA, drug discovery, and power‑grid domains.

AI applicationsGraph Neural NetworkRecommendation Systems

0 likes · 11 min read

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

DataFunSummit

Jun 8, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

This article presents a comprehensive overview of search term recommendation in QQ Browser, covering various recommendation scenarios, challenges, query library architecture, multi‑task ranking models, coarse‑to‑fine ranking pipelines, auto‑completion strategies, and future research directions.

Recommendation Systemsaimachine learning

0 likes · 14 min read

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

DaTaobao Tech

May 31, 2022 · Artificial Intelligence

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

The paper proposes CDAN, a dual‑tower retrieval model that separates item attribute and popularity representations via a Feature Decoupling Module with orthogonal embeddings, aligns head‑tail attribute distributions using MMD and contrastive learning, and jointly trains biased and unbiased towers, achieving higher tail recall, lower exposure concentration, and measurable online click‑through improvements.

Recommendation Systemscontrastive learningdomain adaptation

0 likes · 13 min read

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

HelloTech

May 30, 2022 · Artificial Intelligence

Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights

Harbor’s growth engine combines a passive, attribution‑driven traffic‑allocation algorithm with componentized ranking, search, and marketing systems—using pairwise/Listwise models, multi‑task CTR/CVR prediction, and automated strategy triggers—to align short‑term efficiency with long‑term LTV goals while moving toward causal inference and domain‑expert‑driven general models.

Recommendation Systemsaialgorithm engineering

0 likes · 11 min read

Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights

DataFunSummit

May 26, 2022 · Artificial Intelligence

Exploring Contrastive Learning in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how contrastive learning can alleviate data sparsity and distribution bias in recommendation systems, detailing its theoretical advantages, recent research progress in computer vision and NLP, and a multi‑task self‑supervised framework applied to Kuaishou's short‑video ranking pipeline with significant offline and online performance gains.

KuaishouRecommendation Systemsai

0 likes · 21 min read

Exploring Contrastive Learning in Kuaishou Recommendation Systems

TAL Education Technology

May 26, 2022 · Artificial Intelligence

GoodFuture International Algorithm Team Wins Champion and Runner‑up in the 5th Educational Data Mining Workshop

The GoodFuture International Algorithm Team, together with Jinan University Guangdong Smart Education Research Institute, distinguished themselves among 95 global teams in the 5th Educational Data Mining in Computer Science Education Workshop, securing a champion title in one task and a runner‑up in another, showcasing advanced AI‑driven predictive and recommendation techniques for intelligent student assessment.

Educational Data MiningPredictive ModelingRecommendation Systems

0 likes · 6 min read

GoodFuture International Algorithm Team Wins Champion and Runner‑up in the 5th Educational Data Mining Workshop

HelloTech

May 26, 2022 · Artificial Intelligence

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Hello’s automated C‑side growth algorithm loop integrates diverse traffic sources, semi‑supervised PU‑learning, graph‑based look‑alike targeting, causal uplift models for smart subsidies, and adaptive copy and external ad optimization, dramatically boosting ride‑hailing and lifestyle service revenue while minimizing engineering duplication.

AI PlatformRecommendation SystemsUplift Modeling

0 likes · 20 min read

Hello's Automated Growth Algorithm Loop: C‑Side Scenarios, Challenges, and Active Growth Strategies

Code DAO

May 20, 2022 · Artificial Intelligence

Building a Collaborative Denoising Autoencoder with PyTorch Lightning

This article explains the collaborative denoising autoencoder (CDAE) for recommendation, walks through data preparation with MovieLens, shows a full PyTorch Lightning implementation, tunes hyper‑parameters using Ray Tune and CometML, and reports detailed evaluation metrics.

AutoencoderCDAECometML

0 likes · 11 min read

Building a Collaborative Denoising Autoencoder with PyTorch Lightning

Xiaohongshu Tech REDtech

May 18, 2022 · Artificial Intelligence

Sliding Spectrum Decomposition for Diversified Recommendation in Feed Systems

The paper introduces Sliding Spectrum Decomposition (SSD), a tensor‑based method that quantifies feed diversity through singular‑value volume within sliding windows, integrates quality‑exploration trade‑offs, and employs a hybrid CB2CF model for item embeddings, achieving superior offline and online performance versus DPP in Xiaohongshu’s feed.

DiversityRecommendation Systemsonline A/B testing

0 likes · 10 min read

Sliding Spectrum Decomposition for Diversified Recommendation in Feed Systems

DaTaobao Tech

May 17, 2022 · Artificial Intelligence

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

The paper demonstrates how self‑supervised models SwAV and M6 generate high‑quality image and multimodal embeddings for Meiping Meiwu’s recommendation system, delivering notable gains in scene/style consistency, ranking AUC, classification and retrieval performance, especially for cold‑start items, and achieving measurable production lifts.

A/B testingM6 multimodalRecommendation Systems

0 likes · 15 min read

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

AntTech

May 12, 2022 · Artificial Intelligence

Privacy-Preserving Cross-Domain Recommendation via Differential Privacy and Subspace Embedding

The article reviews a TheWebConf 2022 paper that introduces a two‑stage framework combining differential‑privacy‑based random subspace publishing (using Johnson‑Lindenstrauss and sparse‑aware transforms) with asymmetric deep models to achieve accurate, privacy‑preserving cross‑domain recommendation, and discusses broader differential‑privacy applications.

Privacy-Preserving Machine LearningRecommendation SystemsSubspace Embedding

0 likes · 9 min read

Privacy-Preserving Cross-Domain Recommendation via Differential Privacy and Subspace Embedding

DataFunSummit

May 7, 2022 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training

This article reviews the development of CTR prediction models from early collaborative‑filtering methods to modern deep‑learning approaches, discusses core challenges such as feature interaction and continuous‑feature embedding, introduces recent Huawei solutions like AutoDis and ScaleFreeCTR for efficient large‑embedding training, and outlines future research directions.

Distributed TrainingEmbeddingRecommendation Systems

0 likes · 21 min read

Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training

DataFunTalk

May 7, 2022 · Artificial Intelligence

Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact

This article explains how JD's intelligent recommendation selling point system leverages NLP, BERT, Transformer and pointer‑generator models to automatically create short, personalized product highlights, describing the technical background, system architecture, model training pipeline, online/offline monitoring, and the resulting business benefits.

BERTNLPRecommendation Systems

0 likes · 13 min read

Intelligent Recommendation Selling Point Generation: Architecture, Core AI Techniques, Model Development, and Product Impact

DataFunTalk

May 6, 2022 · Artificial Intelligence

Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation

This article introduces the ESMM (Entire Space Multi‑Task Model) proposed by Alibaba, explaining how it tackles sample selection bias and data sparsity in post‑click conversion rate (CVR) prediction through shared embeddings and implicit pCVR learning, and provides a detailed implementation using the EasyRec framework with code examples.

CVR PredictionESMMRecommendation Systems

0 likes · 11 min read

Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation

Tencent Cloud Developer

Apr 20, 2022 · Artificial Intelligence

Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization

Coarse ranking bridges recall and fine ranking by trimming tens of thousands of candidates to a few hundred or thousand using a three‑part framework—sample construction, ordinary and cross‑feature engineering, and evolving deep models—from rule‑based to lightweight MLPs, while employing distillation, feature crossing, pruning, quantization, and bias mitigation to balance accuracy with strict latency constraints.

Artificial IntelligenceModel OptimizationRecommendation Systems

0 likes · 9 min read

Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization

NetEase Cloud Music Tech Team

Apr 20, 2022 · Artificial Intelligence

Cold Start Solutions for Rich Media Content in Recommendation Systems

The article examines cold‑start challenges for rich‑media recommendations, outlines detection via calibration and lifecycle monitoring, and proposes two remedies—the multi‑stage “rise channel” for promoting fresh content and cross‑modal understanding using CLIP, CB2CF and dual‑tower models—demonstrating NetEase Cloud Music’s 25% distribution boost, over 20% CTR rise, and 40% review‑work reduction.

NetEase Cloud MusicRecommendation Systemscold start

0 likes · 8 min read

Cold Start Solutions for Rich Media Content in Recommendation Systems

DataFunSummit

Apr 11, 2022 · Artificial Intelligence

Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning

This article presents a comprehensive overview of QQ Music's recall pipeline, detailing business characteristics, challenges such as noisy user behavior and cold‑start, and four major solutions—including knowledge‑graph‑enhanced recall, sequence‑based and multi‑interest modeling, audio‑based recall, and federated learning—along with practical insights and Q&A.

Audio EmbeddingRecommendation SystemsSequence Modeling

0 likes · 19 min read

Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning

NetEase Media Technology Team

Apr 11, 2022 · Artificial Intelligence

Multimodal Video Tagging: Challenges and a Two‑Stage Recall‑Ranking Solution

To tackle the massive, multimodal tagging challenge of short‑video platforms—characterized by a huge long‑tail tag set, sparse annotations, and uneven modality contributions—the authors propose a two‑stage recall‑ranking system that first retrieves candidates via text, visual, audio and classification cues, then refines them with contrastive learning and extensive hard‑negative sampling, achieving 0.884 tag accuracy in a real‑world news video recommender.

EmbeddingMultimodal LearningRecommendation Systems

0 likes · 12 min read

Multimodal Video Tagging: Challenges and a Two‑Stage Recall‑Ranking Solution

Alimama Tech

Apr 6, 2022 · Artificial Intelligence

Alibaba's Five Papers Accepted at SIGIR 2022

Alibaba’s research team had five papers accepted at the prestigious SIGIR 2022 conference in Madrid, covering innovations such as joint ad‑ranking and creative selection, personalized bundle generation, calibrated neural predictions, disentangled counterfactual regression, and cold‑start user recommendation, showcasing strong expertise in information retrieval and online advertising.

CalibrationRecommendation SystemsSIGIR 2022

0 likes · 8 min read

Alibaba's Five Papers Accepted at SIGIR 2022

DaTaobao Tech

Apr 6, 2022 · Artificial Intelligence

Improving New User Experience in Taobao Live Recommendation via Multi‑Channel Lifelong Product Sequence Modeling

The paper tackles Taobao Live’s cold‑start problem for new users by introducing a multi‑channel lifelong product‑sequence network that enriches purchase histories with side information, extracts relevance‑focused subsequences across five channels, and integrates them via target‑attention DIN, achieving substantial offline and online performance gains, especially for low‑activity users.

MultimodalRecommendation Systemscold start

0 likes · 23 min read

Improving New User Experience in Taobao Live Recommendation via Multi‑Channel Lifelong Product Sequence Modeling

NetEase Cloud Music Tech Team

Mar 31, 2022 · Industry Insights

How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music

This article details NetEase Cloud Music's technical approach to building implicit user relationship chains—using SimHash, Item2Vec, and MetaPath2Vec embeddings, large‑scale vector search, and a unified service architecture—to address cold‑start challenges across multiple business scenarios.

Item2VecMetaPath2VecRecommendation Systems

0 likes · 20 min read

How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music

DataFunSummit

Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Propensity Score MatchingRecommendation Systemscausal inference

0 likes · 12 min read

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

DataFunSummit

Mar 25, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's e‑commerce recommendation system, detailing its four‑stage ranking pipeline, multi‑objective optimization with personalized fusion, transformer‑based user behavior sequence modeling, fine‑grained behavior modeling, and multimodal feature integration, and shares experimental results and engineering optimizations.

Recommendation Systemse‑commercemulti-objective optimization

0 likes · 17 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

DataFunSummit

Mar 15, 2022 · Artificial Intelligence

KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research

The article introduces KuaiRec, a densely observed short‑video recommendation dataset with 99.6% density covering 1,411 users and 3,327 videos, discusses its structure, advantages over sparse public datasets, and its applicability to unbiased, interactive, conversational and reinforcement‑learning based recommendation studies.

KuaiRecRecommendation Systemsdense dataset

0 likes · 7 min read

KuaiRec: A 99.6% Dense Short‑Video Recommendation Dataset for Unbiased and Interactive Recommendation Research

DataFunSummit

Mar 12, 2022 · Artificial Intelligence

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

This article details Kuaishou's short‑video recommendation pipeline, explaining the challenges of large‑scale sequencing, the development of sequence re‑ranking, multi‑content mixing, on‑device re‑ranking, and reinforcement‑learning‑based strategies, and demonstrates how these innovations improve user engagement and business metrics.

KuaishouRecommendation SystemsReinforcement Learning

0 likes · 15 min read

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

DataFunSummit

Mar 10, 2022 · Artificial Intelligence

Applying Causal Inference to Debias Recommendation Systems at Kuaishou

This talk explores how causal inference techniques are used to identify and mitigate various biases in Kuaishou's recommendation pipeline, covering background theory, recent research advances, practical implementations for popularity and video completion debiasing, and reflections on challenges and future directions.

KuaishouRecommendation Systemsai

0 likes · 19 min read

Applying Causal Inference to Debias Recommendation Systems at Kuaishou

DataFunSummit

Mar 6, 2022 · Artificial Intelligence

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

This article traces the development of embedding methods—from the early word2vec model through item2vec, DeepWalk, Node2vec, EGES, HERec, GraphRT, and target‑fitting approaches like DSSM and YouTube recommendation—highlighting how sequence‑construction and target‑fitting paradigms have shaped modern recommendation systems and AI applications.

Deep LearningEmbeddingItem2Vec

0 likes · 26 min read

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

Alimama Tech

Mar 2, 2022 · Artificial Intelligence

Co-Action Network: A Feature Interaction Model for Click‑Through Rate Prediction

The Co‑Action Network replaces costly Cartesian‑product feature crossing with lightweight micro‑net‑based interaction units that share parameters across feature pairs, delivering comparable CTR prediction accuracy while cutting parameters to one‑tenth and boosting online latency, as proven in large‑scale advertising deployments.

Co-Action NetworkRecommendation Systemsfeature interaction

0 likes · 22 min read

Co-Action Network: A Feature Interaction Model for Click‑Through Rate Prediction

DataFunSummit

Feb 26, 2022 · Artificial Intelligence

Graph-Based Sparse Behavior Recall Models for Content Recommendation

This article presents a comprehensive study of graph‑based recall techniques for content recommendation, detailing how knowledge‑graph‑augmented user‑behavior graphs and novel attention‑driven models such as GADM, SGGA, and SGGGA improve performance for users with sparse interaction histories.

Attention MechanismDeep LearningRecommendation Systems

0 likes · 11 min read

Graph-Based Sparse Behavior Recall Models for Content Recommendation

DaTaobao Tech

Feb 22, 2022 · Artificial Intelligence

Graph-based Deep Recall Models for Sparse User Behavior in Content Recommendation

The paper proposes graph‑based deep recall models that enrich sparse user behavior sequences in video recommendation by integrating content knowledge graphs and adaptive attention mechanisms, demonstrating that variants such as GADM, SGGA, and SGGGA significantly boost click‑through rates in online experiments.

Recommendation Systemsattentiongraph neural networks

0 likes · 11 min read

Graph-based Deep Recall Models for Sparse User Behavior in Content Recommendation

DataFunSummit

Feb 10, 2022 · Artificial Intelligence

Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications

This article introduces Baidu's PGL2.2 graph learning platform, explains graph modeling and message‑passing GNN techniques, details training strategies for small, medium and large graphs, showcases node classification and link‑prediction methods, and describes how the framework is applied in search, recommendation, risk control, and knowledge‑graph competitions.

Knowledge GraphsLarge-Scale TrainingPGL2.2

0 likes · 15 min read

Baidu's PGL2.2: A Graph Neural Network Framework, Techniques, and Real‑World Applications

DataFunSummit

Feb 3, 2022 · Artificial Intelligence

Insights into Recommendation Systems and Their Relation to Computational Advertising

This article examines how recommendation systems have evolved, highlighting the shared matching and ranking mechanisms with computational advertising while also identifying the distinct elements such as ad bidding and multi‑party objectives that differentiate advertising from pure recommendation.

Artificial IntelligenceRecommendation Systemscomputational advertising

0 likes · 11 min read

Insights into Recommendation Systems and Their Relation to Computational Advertising

DataFunTalk

Jan 15, 2022 · Artificial Intelligence

Multimodal + Music: MMatch Series Technologies and Their Applications at Tencent Music

This article presents the multimodal learning demands of QQ Music, introduces the MMatch series of multimodal matching technologies—including image‑text matching, music similarity, AI tagging, and video scoring—and details their practical applications in business scenarios such as merchant public‑play, search, recommendation, and future product ideas.

Artificial IntelligenceMultimodal LearningRecommendation Systems

0 likes · 25 min read

Multimodal + Music: MMatch Series Technologies and Their Applications at Tencent Music

DataFunTalk

Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep LearningRecommendation Systemsfactorization machines

0 likes · 12 min read

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

DataFunSummit

Jan 3, 2022 · Artificial Intelligence

Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)

This article presents an in‑depth overview of Alibaba's e‑commerce and travel recommendation systems, covering the evolution of full‑space CVR estimation models such as ESMM, ESM² and HM³, their architectural components, challenges, and practical applications in the Feizhu platform.

AlibabaCVR estimationFull‑Space Modeling

0 likes · 25 min read

Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)

Alimama Tech

Dec 22, 2021 · Artificial Intelligence

HetMatch: Heterogeneous Graph Neural Network for Keyword Recommendation in Search Advertising

HetMatch is a heterogeneous graph neural network for keyword recommendation in search advertising that tackles cold‑start and large‑scale challenges by hierarchically fusing node and subgraph features, denoising graph convolutions, applying self‑attention, twin matching, and multi‑view learning, delivering notable recall gains and online performance improvements for Alibaba’s advertising tools.

Recommendation Systemscold startheterogeneous graph neural network

0 likes · 14 min read

HetMatch: Heterogeneous Graph Neural Network for Keyword Recommendation in Search Advertising

DataFunTalk

Dec 13, 2021 · Artificial Intelligence

Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval

The article introduces the Dual Vector Foil (DVF) algorithm system, which decouples index construction from model training to enable lightweight, high‑precision large‑scale recall using arbitrary complex models, and details its two‑stage and one‑stage solutions, graph‑based retrieval implementation, performance optimizations, and experimental results.

Deep LearningRecommendation Systemsalgorithm

0 likes · 28 min read

Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval

DataFunSummit

Dec 11, 2021 · Artificial Intelligence

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

This article reviews recent advances in user representation learning for recommender systems, covering self‑supervised pre‑training, lifelong learning, multi‑task modeling, and large‑scale contrastive methods, and provides code and dataset links for key papers such as PeterRec, Conure, DUPN, ShopperBERT, PTUM, UPRec, and LURM.

Recommendation Systemspretrainingself-supervised learning

0 likes · 11 min read

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

21CTO

Dec 9, 2021 · Artificial Intelligence

How Alibaba’s DAMO Academy Is Redefining AI with the First 3D‑Stacked Compute‑Memory Chip

On December 3, Alibaba’s DAMO Academy announced its first AI chip that integrates memory and compute using hybrid‑bond 3D stacking, promising ten‑fold performance gains and 300× energy efficiency for AI workloads such as recommendation systems, and marking a shift from traditional von Neumann designs.

3D stackingAI ChipCompute-in-Memory

0 likes · 5 min read

How Alibaba’s DAMO Academy Is Redefining AI with the First 3D‑Stacked Compute‑Memory Chip

Meituan Technology Team

Dec 9, 2021 · Artificial Intelligence

Deep Customization of TensorFlow for Large-Scale Sparse Training at Meituan

Meituan heavily customized TensorFlow 1.x for large‑scale sparse training, replacing variable embeddings with hash tables, improving load balancing, using RDMA communication, pipeline‑embedding graphs, high‑performance hash tables, and operator merges, achieving over ten‑fold scalability, up to 51% operator speedups, and enabling billions‑parameter models on CPU clusters with future GPU expansion.

Distributed TrainingRecommendation SystemsSparse Parameters

0 likes · 31 min read

Deep Customization of TensorFlow for Large-Scale Sparse Training at Meituan

Alimama Tech

Nov 17, 2021 · Artificial Intelligence

Adaptive Masked Twins-based Layer for Efficient Embedding Dimension Selection in Deep Recommendation Models

AMTL inserts an adaptively‑learned twin‑network mask after each representation layer to prune unnecessary embedding dimensions per feature value, automatically assigning larger sizes to high‑frequency features, achieving higher CTR accuracy, about 60% storage reduction, and seamless hot‑starting across recommendation models.

EmbeddingRecommendation Systemsadaptive masking

0 likes · 15 min read

Adaptive Masked Twins-based Layer for Efficient Embedding Dimension Selection in Deep Recommendation Models

DataFunTalk

Nov 10, 2021 · Artificial Intelligence

Learnable Index Structures for Large‑Scale Retrieval: Deep Retrieval Model and Training Methods

This article introduces ByteDance's Deep Retrieval (DR) framework, describing its learnable index structure that aligns embedding training with retrieval objectives, detailing the core model, structure‑loss training via EM and online EM algorithms, beam‑search serving, multi‑task learning, and practical insights from Q&A.

Beam SearchEM algorithmRecommendation Systems

0 likes · 11 min read

Learnable Index Structures for Large‑Scale Retrieval: Deep Retrieval Model and Training Methods

DataFunSummit

Nov 5, 2021 · Artificial Intelligence

Practical Insights into Online Experiment Design and Analysis at Tencent Lookpoint

The presentation offers a comprehensive overview of online experiment fundamentals, design variations, and real-world case studies from Tencent Lookpoint, emphasizing hypothesis validation, causal analysis, best practices, and actionable recommendations for improving product growth and decision‑making.

A/B testingData ScienceRecommendation Systems

0 likes · 20 min read

Practical Insights into Online Experiment Design and Analysis at Tencent Lookpoint

DataFunTalk

Nov 2, 2021 · Artificial Intelligence

Personalized Recommendation and Advertising Algorithms for E‑commerce: Business Overview, Recall and Ranking Optimization, Multi‑Task Modeling, and Future Directions

This article presents a comprehensive technical overview of JD.com’s e‑commerce recommendation and advertising systems, covering business scenarios, recall optimizations (profile and similarity‑based), multi‑task ranking improvements, sample weighting, multi‑model ensembles, PID‑based CPC control, conversion‑delay modeling, and the achieved performance gains and future research plans.

CTR optimizationRecommendation Systemse‑commerce

0 likes · 18 min read

Personalized Recommendation and Advertising Algorithms for E‑commerce: Business Overview, Recall and Ranking Optimization, Multi‑Task Modeling, and Future Directions

DataFunTalk

Nov 1, 2021 · Product Management

Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform

This article presents a comprehensive overview of online experiment design and analysis, covering basic definitions, AB testing principles, complex experiment types, real-world case studies from Tencent's information flow platform, and practical guidelines for reliable experiment evaluation and product decision‑making.

A/B testingRecommendation Systemscausal inference

0 likes · 21 min read

Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform

DataFunSummit

Oct 29, 2021 · Artificial Intelligence

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

This talk explains contrastive learning fundamentals, typical image‑domain models such as SimCLR, MoCo and SwAV, and shows how their principles—positive/negative sample construction, encoder design, loss functions, alignment and uniformity—can be applied to improve dual‑tower retrieval and ranking models, embedding normalization, temperature scaling, and graph‑based recommender systems.

InfoNCERecommendation Systemscontrastive learning

0 likes · 40 min read

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

DataFunTalk

Oct 26, 2021 · Artificial Intelligence

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

This article explains how contrastive learning, originally popular in computer‑vision, can be interpreted and applied to recommendation‑system recall and coarse‑ranking models, covering its theoretical roots, typical architectures like SimCLR, MoCo and SwAV, and practical tricks such as in‑batch negatives, embedding normalization, temperature scaling, and graph‑based extensions.

Recommendation Systemscontrastive learningdual-tower models

0 likes · 40 min read

Contrastive Learning Perspective on Retrieval and Reranking Models in Recommendation Systems

Volcano Engine Developer Services

Sep 25, 2021 · Artificial Intelligence

Cutting‑Edge AI from ByteDance & OPPO: Audio, NLP, and Translation

The ByteDance Engine Developer Community Meetup featured senior engineers from ByteDance and OPPO who presented the latest advances in intelligent audio signal processing, natural language processing for recommendation, entity linking in knowledge graphs, and multimedia machine translation, highlighting practical applications and performance challenges.

Artificial IntelligenceRecommendation Systemsknowledge graph

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Cutting‑Edge AI from ByteDance & OPPO: Audio, NLP, and Translation

Alimama Tech

Sep 8, 2021 · Artificial Intelligence

Engineering Optimizations for Large‑Scale Advertising Recall Models: Full‑Cache Scoring and Index Flattening

Alibaba Mama’s advertising platform modernized its Tree‑based Deep Model by introducing a dual‑tower full‑library DNN with aggressive pre‑filtering and custom GPU TopK kernels, and a flattened‑tree model that retains beam search with multi‑head attention, while applying memory‑aware tricks such as attention swapping, softmax approximation, tiled‑matmul splitting, TensorCore batching, INT8 quantization and cache‑resident ad vectors, enabling multi‑fold latency reductions with minimal recall loss.

Beam SearchGPU AccelerationModel Optimization

0 likes · 15 min read

Engineering Optimizations for Large‑Scale Advertising Recall Models: Full‑Cache Scoring and Index Flattening

Laravel Tech Community

Sep 5, 2021 · Artificial Intelligence

Comprehensive Collection of Open Data Sources and Datasets for AI and Data Analysis

This article provides a curated list of publicly available data query websites, simple universal datasets, large-scale collections, and specialized datasets for machine learning, image classification, text classification, and recommendation systems, offering valuable resources for AI research and data-driven projects.

Artificial IntelligenceBig DataDatasets

0 likes · 7 min read

Comprehensive Collection of Open Data Sources and Datasets for AI and Data Analysis

DataFunTalk

Sep 3, 2021 · Artificial Intelligence

Construction and Application of an Interest Point Graph for Content Understanding in Information Feed Recommendation

This article explains how large‑scale UGC data is used to build a multi‑type interest point graph, describes the mining, hierarchical and associative relationship extraction methods, and demonstrates how the graph improves content understanding and recommendation accuracy while mitigating filter‑bubble effects.

Artificial IntelligenceRecommendation Systemscontent understanding

0 likes · 25 min read

Construction and Application of an Interest Point Graph for Content Understanding in Information Feed Recommendation

DataFunSummit

Sep 2, 2021 · Artificial Intelligence

Multi‑Task Learning Models for Recommendation Systems: An Industrial Survey

This article surveys recent industrial multi‑task learning approaches for recommendation, covering models such as Alibaba's ESMM and ESM2, DUPN, Meituan's deep ranking, Google’s MMoE, YouTube’s multi‑objective system, Zhihu’s ranking, and summarizing their architectures, loss functions, and practical gains.

CTRCVRMMoE

0 likes · 15 min read

Multi‑Task Learning Models for Recommendation Systems: An Industrial Survey

DataFunSummit

Aug 7, 2021 · Artificial Intelligence

Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

This article describes how Alibaba's advertising team tackled the challenges of modeling long‑term user interests for CTR prediction by co‑designing incremental computation services, introducing memory‑network‑based models (MIMN and HPMN), and achieving significant offline and online performance gains.

CTR predictionLong-Term InterestRecommendation Systems

0 likes · 17 min read

Long-Term User Interest Modeling for Click-Through Rate Prediction in Alibaba's Advertising System

DataFunSummit

Jul 24, 2021 · Artificial Intelligence

Alibaba 1688 User Growth, Full‑Chain Growth System, and Deep‑Learning Applications in Search and Promotion

This article presents a comprehensive overview of Alibaba 1688's user‑growth strategy, detailing lifecycle segmentation, budget‑constrained installation optimization, intelligent red‑packet allocation, smart push mechanisms, information‑flow advertising, and the deep‑learning‑driven search pipeline that together power the platform's growth engine.

Recommendation Systemsbudget optimizatione‑commerce

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Alibaba 1688 User Growth, Full‑Chain Growth System, and Deep‑Learning Applications in Search and Promotion

DataFunTalk

Jul 17, 2021 · Artificial Intelligence

Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions

This article describes the evolution of a multi‑objective deep‑learning framework for automatically assigning CRM opportunities to salespeople, detailing five model versions—from an XGBoost baseline with sample weighting to advanced PLE‑based architectures—while reporting offline and online performance gains in both call‑out and connection‑out conversion rates.

A/B testingCRMDeep Learning

0 likes · 33 min read

Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions

Beike Product & Technology

Jul 8, 2021 · Artificial Intelligence

Applying End-to-End Deep Learning Models for Real Estate Agent Churn Prediction

This article reviews the evolution of end-to-end deep learning models, describes how they were adapted and optimized for a real‑estate broker churn‑warning scenario, and presents experimental results showing significant improvements in AUC, KS and lift over traditional classifiers.

Recommendation SystemsTime Serieschurn prediction

0 likes · 17 min read

Applying End-to-End Deep Learning Models for Real Estate Agent Churn Prediction

Meituan Technology Team

Jul 8, 2021 · Artificial Intelligence

Multi-Business Ranking Modeling in Meituan Search

Meituan Search tackles the multi‑business ranking challenge by introducing a quota‑allocation model (MQM) and a series of precise ranking models (MBN) that progressively incorporate sub‑networks, multi‑task learning and transformer‑based behavior sequences, delivering consistent CTR and purchase‑rate gains across food, hotel, travel and other services while outlining future work on feature utilization, sample‑imbalance mitigation and multi‑objective optimization.

MeituanRecommendation Systemsmachine learning

0 likes · 15 min read

Multi-Business Ranking Modeling in Meituan Search

DataFunTalk

Jun 15, 2021 · Artificial Intelligence

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

The paper introduces PAPERec, a personalized multi‑objective recommendation framework that leverages Pareto‑oriented reinforcement learning to generate user‑specific objective weights, enabling the model to approximate Pareto‑optimal solutions and achieve superior click‑through rate and dwell‑time performance in both offline and online experiments.

CTRPareto efficiencyRecommendation Systems

0 likes · 12 min read

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

DataFunTalk

Jun 2, 2021 · Artificial Intelligence

Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model

This article introduces JD.com's 9N GRAPH industrialization framework for large‑scale graph algorithms in advertising, covering the challenges of e‑commerce recommendation, the end‑to‑end solution architecture, the BVSHG multi‑behavior heterogeneous GNN model, training pipelines, and observed business impact.

BVSHGIndustrial AIJD.com

0 likes · 17 min read

Industrial-Scale Graph Learning for JD Advertising: 9N GRAPH End‑to‑End Solution and BVSHG Model

Alimama Tech

May 27, 2021 · Artificial Intelligence

Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction (PCF‑GNN)

PCF‑GNN builds a heterogeneous graph of feature nodes and learns edge statistics via pre‑training, enabling it to infer unseen cross‑features, reduce storage by over 50%, and consistently improve CTR prediction AUC compared to implicit and explicit baselines, with proven online gains.

Graph Neural NetworkRecommendation Systemscross feature

0 likes · 12 min read

Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction (PCF‑GNN)

iQIYI Technical Product Team

May 14, 2021 · Artificial Intelligence

Performance Optimization of TensorFlow Feature Columns in Recommendation Systems

The article details how iQIYI doubled online inference speed and cut p99 latency by over 50% in TensorFlow‑based CTR recommendation models by replacing costly string‑based integer hashing, removing redundant dense‑sparse conversions, and deduplicating user features for efficient broadcasting, demonstrating that modest Feature Column tweaks can yield major production gains.

Feature ColumnsRecommendation SystemsTensorFlow

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Performance Optimization of TensorFlow Feature Columns in Recommendation Systems

DataFunTalk

Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Recommendation SystemsTransformerbias mitigation

0 likes · 22 min read

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

DataFunSummit

Apr 15, 2021 · Artificial Intelligence

Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)

The 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a submission deadline of May 10 2021 and results announced on June 10 2021.

KDDRecommendation SystemsSparse Data

0 likes · 6 min read

Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)

DataFunTalk

Apr 3, 2021 · Artificial Intelligence

A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising

User behavior sequence modeling, crucial for search and recommendation advertising ranking, has evolved from simple pooling to attention, RNN, capsule, and Transformer architectures, with industrial applications across e‑commerce, social, video, and music platforms, and future directions include time‑aware, multi‑dimensional, and self‑supervised approaches.

Deep LearningRecommendation SystemsSequence Modeling

0 likes · 24 min read

A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising

Bitu Technology

Mar 26, 2021 · Artificial Intelligence

Applying Machine Learning to Advertising‑Based Video‑On‑Demand (AVOD) at Tubi

This article explains how Tubi leverages machine learning—particularly PyTorch, Databricks, and cloud services—to improve content understanding, advertising technology, and recommendation systems within its advertising‑based video‑on‑demand platform, outlining the three AVOD pillars, technical stack, and future research directions.

AVODDatabricksPyTorch

0 likes · 13 min read

Applying Machine Learning to Advertising‑Based Video‑On‑Demand (AVOD) at Tubi

DataFunTalk

Mar 20, 2021 · Artificial Intelligence

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

This article presents a comprehensive technical overview of Momo's model‑based recall system for social recommendation, detailing the underlying user‑scenario behavior models, social graph embeddings, multimodal content semantics, and deployment results that improve matching relevance and user interaction rates.

EmbeddingGraph Neural NetworkMomo

0 likes · 19 min read

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

DataFunTalk

Mar 17, 2021 · Artificial Intelligence

Deep Ranking Model Evolution and Applications in Taobao Live: DBMTL, DMR, and RUI Ranking

This article presents a comprehensive overview of Taobao Live's deep ranking system evolution, detailing the DBMTL multi‑task learning framework, the two‑tower DMR matching‑ranking architecture, and the RUI Ranking refer‑item model, together with their offline formulas, online deployment scenarios, and measured performance gains across click‑through, watch‑time, and conversion metrics.

Deep LearningModel OptimizationRecommendation Systems

0 likes · 27 min read

Deep Ranking Model Evolution and Applications in Taobao Live: DBMTL, DMR, and RUI Ranking

DataFunSummit

Mar 11, 2021 · Artificial Intelligence

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

This article presents the Search‑Based Interest Model (SIM), a two‑stage retrieval framework that indexes a user's entire behavior history to enable long‑term interest modeling for click‑through‑rate prediction, demonstrating practical deployment and improved recommendation of long‑term interests in e‑commerce.

CTR predictionLong-Term InterestRecommendation Systems

0 likes · 16 min read

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

DataFunSummit

Mar 7, 2021 · Artificial Intelligence

A Comprehensive Overview of Multi‑Task Learning in AI: Concepts, Applications, and Practical Tips

This article provides an in‑depth introduction to multi‑task learning (MTL), explaining its core concepts, why it is widely used in recommendation systems, NLP, CV and reinforcement learning, and offering guidance on model architectures, loss design, auxiliary tasks, and practical deployment tips.

MTLNLPRecommendation Systems

0 likes · 19 min read

A Comprehensive Overview of Multi‑Task Learning in AI: Concepts, Applications, and Practical Tips

Sohu Tech Products

Feb 24, 2021 · Artificial Intelligence

EdgeRec: Edge Computing in Recommendation Systems

EdgeRec explores how moving recommendation system components to the edge—leveraging real‑time user behavior, heterogeneous action modeling, on‑device reranking, mixed‑ranking, and personalized “thousand‑person‑one‑model” training—can reduce latency, improve relevance, and boost business metrics compared to traditional cloud‑centric pipelines.

Edge ComputingMeta LearningMobile AI

0 likes · 19 min read

EdgeRec: Edge Computing in Recommendation Systems

DataFunTalk

Feb 13, 2021 · Artificial Intelligence

Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations

This article details how 58.com tackled the challenges of multi‑business recommendation on its home page by developing a dual‑channel deep interest model, introducing customized feature‑crossing, optimizing training and online performance, and exploring multi‑channel extensions for broader scenario adaptation.

Deep LearningRecommendation Systemsai

0 likes · 20 min read

Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations

DataFunTalk

Feb 10, 2021 · Artificial Intelligence

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

This article describes how 58.com’s rental platform leverages deep learning models such as Wide&Deep, DeepFM, DCN, DIN, and DIEN to improve search ranking, detailing data pipelines, feature engineering, model iteration, multi‑task training, prediction optimizations, and resulting online performance gains.

Deep LearningModel OptimizationRecommendation Systems

0 likes · 27 min read

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

JD Cloud Developers

Feb 10, 2021 · Artificial Intelligence

How JD Tech’s Breakthrough AI Papers Dominated AAAI 2021

JD Tech showcased a remarkable 21-paper presence at AAAI 2021, covering federated learning, spatio‑temporal AI, recommendation systems, computer vision, and causal learning, highlighting the company’s transition from research to real‑world AI applications across smart cities, retail, and risk management.

AAAI 2021Computer VisionFederated Learning

0 likes · 12 min read

How JD Tech’s Breakthrough AI Papers Dominated AAAI 2021

DataFunSummit

Feb 7, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article explains how Fliggy combines interactive recommendation with travel‑theme recommendation, detailing the underlying algorithms, user‑demand classification, real‑time interest capture, recall strategies, multi‑task learning for CTR prediction, and engineering tricks that improve personalization and click‑through rates.

AlibabaFliggyRecommendation Systems

0 likes · 17 min read

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

DataFunTalk

Jan 29, 2021 · Artificial Intelligence

Content Embedding Practices and Challenges at Hulu

This article presents Hulu's multi‑layered approach to content understanding and embedding, describing tag‑based graph embeddings, metadata‑BERT enhancements, multimodal video/audio feature aggregation, and various applications such as similarity search, ranking, cold‑start retrieval, and collection modeling, while also discussing current limitations and open research questions.

HuluRecommendation Systemscontent embedding

0 likes · 12 min read

Content Embedding Practices and Challenges at Hulu

DeWu Technology

Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Deep LearningEmbeddingRecommendation Systems

0 likes · 13 min read

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

DataFunTalk

Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeDeep LearningEmbedding

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User Preference Mining and Modeling Practices at Beike

DataFunTalk

Jan 1, 2021 · Artificial Intelligence

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.

Artificial IntelligenceRecommendation Systemsgraph embedding

0 likes · 12 min read

Hot Topic Mining and Expansion Using User‑Behavior Graph Embedding for Recommendation Systems

DataFunTalk

Dec 31, 2020 · Artificial Intelligence

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

This article introduces graph neural networks, explains their underlying sampling and aggregation mechanisms, and demonstrates how they are applied in large‑scale recommendation scenarios such as video and content feeds at Tencent, highlighting practical results and lessons learned.

Artificial IntelligenceBig DataGraphSAGE

0 likes · 10 min read

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

DataFunSummit

Dec 29, 2020 · Artificial Intelligence

Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice

This article introduces graph neural networks, explains their fundamentals and GraphSAGE/DGI algorithms, and demonstrates how Tencent applies them to recommendation scenarios such as video and WeChat content, highlighting network construction, feature engineering, sampling and aggregation techniques, and practical performance gains.

DGIGraphSAGERecommendation Systems

0 likes · 8 min read

Graph Neural Networks for Recommendation: Principles, Frameworks, and Tencent Practice

DataFunTalk

Dec 29, 2020 · Artificial Intelligence

Algorithmic Insights into Free Novel Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal Modeling

This article examines the unique properties of novel literature and the difficulties of tag‑based recommendation, then details multi‑modal feature representation, dual‑tower semantic modeling, clustering, and YouTube‑style DNN recall techniques used to improve free novel recommendation systems.

Recommendation Systemsaidual-tower model

0 likes · 9 min read

Algorithmic Insights into Free Novel Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal Modeling

DataFunTalk

Dec 24, 2020 · Artificial Intelligence

Applying Angel Recommendation Algorithms to Game Recommendation: From Linear Classic Models to DeepFM

This article explains how Tencent's Angel platform powers game recommendation on Steam, Wegame, and the Tesla platform, comparing traditional linear tag‑based and collaborative‑filtering methods with the non‑linear DeepFM model and offering practical deployment tips.

Recommendation Systemsaicollaborative filtering

0 likes · 9 min read

Applying Angel Recommendation Algorithms to Game Recommendation: From Linear Classic Models to DeepFM