Tagged articles

Recommendation Systems

489 articles · Page 3 of 5

Jul 18, 2023 · Artificial Intelligence

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

This article presents Fliggy's work on user travel demand prediction, outlining the unique challenges of travel scenarios, the evolution of recall and ranking algorithms—including multi‑task learning, graph‑based models, and intention‑capture mechanisms—and discusses future research directions such as long‑sequence modeling and cross‑domain learning.

Recommendation Systemsgraph neural networksmachine learning

0 likes · 19 min read

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

DataFunTalk

Jul 16, 2023 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article introduces graph neural networks, explains graph representation learning, discusses their evolution from random walks to spectral and spatial convolutions, and details how OPPO applies GNNs to improve recommendation system recall and ranking, highlighting practical architecture, experimental gains, and future research directions.

OPPORecommendation Systemsgraph neural networks

0 likes · 19 min read

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

DataFunTalk

Jul 6, 2023 · Artificial Intelligence

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

This article presents Tencent TRS's industrial deployment of meta‑learning and cross‑domain recommendation, detailing problem definitions, solution architectures, challenges of industrialization, and practical implementations that achieve personalized modeling and cost‑effective multi‑scene recommendation across various online services.

MAMLRecommendation Systemscross-domain

0 likes · 18 min read

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

DataFunSummit

Jul 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, causal embedding techniques, experimental results on MovieLens and Yelp, and future research directions.

Recommendation Systemscausal inferenceconsumer perspective

0 likes · 9 min read

Fairness in Recommendation Systems: Consumer and Provider Perspectives

DataFunSummit

Jun 30, 2023 · Artificial Intelligence

Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions

In this expert roundtable, leading researchers and engineers discuss the current state of recommendation systems, how large language models can reshape the field, the technical and practical challenges involved, and practical advice for practitioners looking to adopt AI‑driven personalization solutions.

AIIndustry InsightsLarge Language Models

0 likes · 36 min read

Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions

DataFunTalk

Jun 20, 2023 · Artificial Intelligence

How Recommendation Systems Work and Their Integration with ChatGPT

This article explains the fundamentals of recommendation systems, their digital representation, how ChatGPT and large language models are applied to enhance recommendation performance, and highlights emerging trends such as conversational recommendation and a recommended book on the subject.

AIChatGPTConversational AI

0 likes · 8 min read

How Recommendation Systems Work and Their Integration with ChatGPT

58 Tech

May 26, 2023 · Artificial Intelligence

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

The A2M Summit announcement details a 2023 AI and machine learning conference where senior algorithm architect Liu Lixi presents his talk on practical recommendation system techniques for sparse data, low‑frequency scenarios, and ad‑creative optimization within 58.com’s industry‑wide digital transformation.

58.comAIIndustrial Transformation

0 likes · 5 min read

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

DataFunTalk

May 8, 2023 · Artificial Intelligence

Comprehensive Overview of Modern Recommendation System Technologies

This article presents a detailed survey of recent advances in recommendation system technology, covering system architecture, user understanding layers, various recall methods, ranking techniques, auxiliary algorithms such as cold-start and bias modeling, and evaluation metrics, with references to industry practices and academic research.

AIRecommendation SystemsUser Modeling

0 likes · 13 min read

Comprehensive Overview of Modern Recommendation System Technologies

DaTaobao Tech

Apr 28, 2023 · Artificial Intelligence

Multi-Scenario Recommendation Model

The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.

AIData ModelingPersonalized Recommendation

0 likes · 23 min read

Kuaishou Tech

Apr 28, 2023 · Artificial Intelligence

How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems

This article presents the Hyper‑Actor Critic (HAC) framework that splits reinforcement‑learning policies into continuous hyper‑actions and effective recommendation lists, introduces alignment and supervised losses, and demonstrates superior performance on an online simulator compared to existing RL and supervised methods.

AI researchRecommendation Systemshyper-actor critic

0 likes · 9 min read

How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems

DataFunTalk

Apr 24, 2023 · Artificial Intelligence

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

This article reviews the evolution of Weibo's large‑scale recommendation technology, covering the system's business scenarios, technical roadmap, recent large model iterations, multi‑task and multi‑scenario modeling, feature engineering, consistency between recall and ranking, and emerging techniques such as causal inference and graph methods.

Recommendation Systemscausal inferencegraph embeddings

0 likes · 18 min read

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

Kuaishou Tech

Apr 23, 2023 · Artificial Intelligence

Kuaishou & Renmin AI Institute: Driving Multimodal Large Model Innovation

The article details how Kuaishou’s multimodal AI research, including its K7 trillion‑parameter model and VLUA algorithm, partners with Renmin University’s Gaoling AI Institute to launch a joint lab, produce cutting‑edge papers such as WebBrain and ChatImg, and advance recommendation and search technologies across the short‑video ecosystem.

AILarge Language ModelsMultimodal Models

0 likes · 17 min read

Kuaishou & Renmin AI Institute: Driving Multimodal Large Model Innovation

DataFunTalk

Apr 22, 2023 · Artificial Intelligence

Career Planning for Algorithm Engineers: Stages, Strategies, and Technical Development

This article provides algorithm engineers with a comprehensive career roadmap covering three key professional stages, practical planning advice, technical skill sharpening, industry trend analysis, and a detailed Q&A to help navigate the volatile tech job market.

AIAlgorithm EngineeringIndustry Trends

0 likes · 28 min read

Career Planning for Algorithm Engineers: Stages, Strategies, and Technical Development

DataFunSummit

Apr 14, 2023 · Big Data

An Overview of User Profiling: Definitions, Elements, Types, Dimensions, Applications, and Development Process

This article provides a comprehensive introduction to user profiling, covering its definition, key elements, classification types, common dimensions, practical application scenarios, lifecycle considerations, development workflow, and validation methods for building effective data‑driven user models.

Big DataData AnalysisRecommendation Systems

0 likes · 10 min read

An Overview of User Profiling: Definitions, Elements, Types, Dimensions, Applications, and Development Process

DataFunTalk

Apr 10, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

This article presents a comprehensive study of multi‑scenario personalized recommendation, introducing a scenario‑adaptive and self‑supervised model (SASS) that jointly addresses data sparsity, domain adaptation, and recall‑stage deployment through a two‑stage training pipeline and extensive experiments on Alibaba’s Taobao platform.

AlibabaRecommendation SystemsSelf-supervised Learning

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

Xiaohongshu Tech REDtech

Apr 3, 2023 · Industry Insights

What Drives Intelligent Recommendation and Search? Key Takeaways from Xiaohongshu’s CCF C³ Event

The CCF C³ event at Xiaohongshu gathered leading researchers and industry experts to dissect the latest advances, challenges, and future opportunities in intelligent recommendation and search, including multimodal content handling, decentralized distribution, cold‑start solutions, and the impact of large language models.

AIIndustry InsightsLarge Language Models

0 likes · 11 min read

What Drives Intelligent Recommendation and Search? Key Takeaways from Xiaohongshu’s CCF C³ Event

Kuaishou Large Model

Mar 31, 2023 · Artificial Intelligence

How Kuaishou Elevates Video Quality and AI Performance at NVIDIA GTC 2023

At NVIDIA GTC 2023, Kuaishou engineers unveiled cutting‑edge solutions ranging from video quality assessment and enhancement, 3D digital‑human live streaming, a custom TensorRT‑based performance framework, large‑scale recommendation model acceleration, to multimodal massive‑model deployment for short‑video scenarios.

Multimodal ModelsRecommendation SystemsTensorRT

0 likes · 9 min read

How Kuaishou Elevates Video Quality and AI Performance at NVIDIA GTC 2023

DaTaobao Tech

Mar 24, 2023 · Artificial Intelligence

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

In a March 27 livestream, Alibaba senior algorithm engineer Chen Zhihong will explain how popularity bias affects recommendation pipelines, review existing mitigation techniques, and introduce a decoupled domain‑adaptive unbiased dual‑tower recall model that leverages bias while preserving recommendation fairness.

Recommendation SystemsUnbiased Recallmachine learning

0 likes · 2 min read

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

Alibaba Cloud Infrastructure

Mar 22, 2023 · Artificial Intelligence

CUTLASS Extreme Performance Optimization and Its Application in Alibaba's Recommendation System

At the GTC conference, the talk presents Alibaba Cloud’s heterogeneous computing platform and introduces the Open Deep Learning API (ODLA), then details how CUTLASS‑based operator fusion dramatically accelerates attention and MLP layers in large‑scale recommendation models, achieving multi‑fold performance gains in production.

CutlassGPU computingRecommendation Systems

0 likes · 5 min read

CUTLASS Extreme Performance Optimization and Its Application in Alibaba's Recommendation System

Huawei Cloud Developer Alliance

Mar 18, 2023 · Artificial Intelligence

Unveiling NetEase’s ‘YuZhi’ Multimodal Model: Boosting Personalized Recommendations

NetEase’s Fuxi team developed the multimodal ‘YuZhi’ model, a large‑scale image‑text dual‑tower system optimized with the EET inference framework, which powers personalized recommendations in NetEase News and Cloud Music, while a partnership with Huawei Ascend AI and MindSpore enables further model acceleration, compression, and the new ‘YuZhi‑Wukong’ model that improves video recommendation metrics by about 5%.

Huawei Ascend AIMindSporeRecommendation Systems

0 likes · 5 min read

Unveiling NetEase’s ‘YuZhi’ Multimodal Model: Boosting Personalized Recommendations

JD Cloud Developers

Feb 27, 2023 · Artificial Intelligence

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

The article explains JD’s Explore & Exploit (EE) module, its bias‑related challenges, the iterative optimization loop, model debiasing techniques for position and popularity bias, personalized bias modeling, causal inference methods, online AB results, and offline evaluation metrics, highlighting significant improvements in search diversity and efficiency.

Bias MitigationEE moduleRecommendation Systems

0 likes · 16 min read

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

DaTaobao Tech

Feb 13, 2023 · Artificial Intelligence

Why Recommendation Systems Matter: From Basics to Advanced Strategies

This article explains what recommendation systems are, their core tasks, evaluation metrics, popular algorithms such as collaborative filtering and latent factor models, how to handle cold‑start and contextual challenges, the role of social networks, and typical system architecture, providing a comprehensive overview for beginners and practitioners.

Collaborative FilteringRecommendation Systemscold start

0 likes · 21 min read

Why Recommendation Systems Matter: From Basics to Advanced Strategies

DataFunTalk

Feb 5, 2023 · Artificial Intelligence

A Six‑Year Retrospective on Deep Learning Algorithms and Their Applications

This article reviews the author’s six‑year hands‑on experience with deep learning, covering breakthroughs in speech recognition, computer vision, language modeling, reinforcement learning, privacy protection, model compression, recommendation systems, and future research directions, while summarizing technical lessons and practical insights.

AIRecommendation Systemsmodel compression

0 likes · 30 min read

A Six‑Year Retrospective on Deep Learning Algorithms and Their Applications

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

This article presents a comprehensive overview of the background, current multi‑path recall methods, and a series of practical optimizations—including dual‑tower models, enhanced vectors, an unbiased IPW‑based framework, and a travel‑state‑aware deep recall model—applied to Feizhu's homepage recommendation system, with both offline and online experimental results demonstrating click‑through rate improvements.

Bias MitigationRecommendation Systemsdual-tower model

0 likes · 17 min read

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Between Heaven and Earth: Reflections of an Algorithm Engineer

The article argues that algorithm engineers should move beyond a narrow focus on deep‑learning models, emphasizing the importance of system architecture, data quality, and thoughtful problem framing to break through performance plateaus in advertising and recommendation systems.

AdvertisingAlgorithm EngineeringRecommendation Systems

0 likes · 10 min read

Between Heaven and Earth: Reflections of an Algorithm Engineer

DataFunTalk

Jan 21, 2023 · Artificial Intelligence

Challenges and Best Practices in Recommendation Systems – Expert Interview

This interview with three recommendation‑system experts explores the technical architecture, data sources, feature engineering, recall and ranking strategies, evaluation metrics, cold‑start solutions, and practical difficulties, offering actionable insights to avoid common pitfalls in real‑world recommender deployments.

Multi-modalRankingRecommendation Systems

0 likes · 15 min read

Challenges and Best Practices in Recommendation Systems – Expert Interview

Bilibili Tech

Dec 20, 2022 · Industry Insights

Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study

This article presents a detailed study on applying recommendation‑system techniques to test case prioritization for Bilibili's mobile apps, describing the problem definition, evaluation metrics, data processing, FM model selection, experimental results, practical deployment, and future research directions.

BilibiliRecommendation SystemsSoftware testing

0 likes · 13 min read

Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study

Alibaba Cloud Big Data AI Platform

Dec 15, 2022 · Artificial Intelligence

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%

Vivo’s AI recommendation team leveraged Alibaba’s DeepRec engine—introducing dynamic Embedding Variables, feature admission/elimination, Parquet datasets, and advanced CPU/GPU inference optimizations such as SessionGroup, device placement, multi‑stream and BladeDISC compilation—resulting in notable gains in model accuracy, latency reduction, and resource efficiency.

DeepRecGPU inferenceRecommendation Systems

0 likes · 13 min read

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%

HomeTech

Dec 9, 2022 · Artificial Intelligence

Interview with Li Benyang: AI, Knowledge Graphs, and Career Insights in Intelligent Recommendation

In this interview, Li Benyang, head of the intelligent recommendation content understanding team at Autohome, shares his AI background, the evolution of recommendation systems, knowledge‑graph construction for automotive content, career choices between big firms and startups, and practical advice for technologists navigating fast‑changing industries.

AIKnowledge GraphRecommendation Systems

0 likes · 12 min read

Interview with Li Benyang: AI, Knowledge Graphs, and Career Insights in Intelligent Recommendation

NetEase LeiHuo UX Big Data Technology

Nov 22, 2022 · Artificial Intelligence

Sample Weighting in Machine Learning: From YouTube Playback Duration to Game Recommendation Optimization

This article explains why and how sample weighting is used in machine learning, illustrates YouTube's conversion of video watch time into sample weights to align with its commercial goals, and describes practical weighted‑logistic‑regression techniques applied to improve game recommendation systems.

AIRecommendation SystemsYouTube

0 likes · 8 min read

Sample Weighting in Machine Learning: From YouTube Playback Duration to Game Recommendation Optimization

DataFunSummit

Nov 3, 2022 · Artificial Intelligence

Applying NVIDIA MPS to Boost GPU Utilization for Recommendation Inference

This article explains why traditional CPU inference and naïve GPU usage are inefficient for recommendation workloads, introduces NVIDIA Multi‑Process Service (MPS) technology, describes VIVO's custom Rust‑based inference engine and deployment strategies, and presents performance and cost benefits along with practical deployment considerations.

GPU inferenceKubernetesMPS

0 likes · 13 min read

Applying NVIDIA MPS to Boost GPU Utilization for Recommendation Inference

DataFunTalk

Nov 1, 2022 · Artificial Intelligence

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

This article presents the practical evolution of NetEase Yanxuan's recommendation pipeline, covering background, multi‑objective and cross‑domain modeling, bias correction, loss function enhancements, long‑term value strategies, and multi‑scene modeling, with experimental results and a Q&A session.

AIBias CorrectionMMoE

0 likes · 20 min read

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

Alimama Tech

Oct 19, 2022 · Artificial Intelligence

Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models

The study reveals that industrial deep click‑through‑rate models often overfit dramatically after the first training epoch—a “one‑epoch phenomenon” caused by the embedding‑plus‑MLP architecture, fast optimizers, and highly sparse features, with performance dropping sharply unless sparsity is reduced or training is limited to a single pass.

CTREmbeddingMLP

0 likes · 15 min read

Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models

Model Perspective

Oct 14, 2022 · Artificial Intelligence

How SimRank Leverages Graph Theory for Powerful Recommendations

SimRank, a graph‑theoretic recommendation algorithm, models users and items as a bipartite graph and computes similarity through iterative matrix operations, with extensions like SimRank++ incorporating edge weights and evidence, while scalable solutions use big‑data frameworks or Monte‑Carlo simulations.

Big DataMatrix ComputationRecommendation Systems

0 likes · 8 min read

How SimRank Leverages Graph Theory for Powerful Recommendations

DataFunTalk

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 modelsFeature ImportanceRecommendation Systems

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.

AIModel OptimizationRecommendation Systems

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.

NeurIPSOnline AdvertisingRecommendation Systems

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 SystemsSelf-supervised Learning

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.

AIRecommendation Systemsdata 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 recallRankingRecommendation 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 SystemsTransfer 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.

AIRecommendation Systemsdata 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-ExploitationRecommendation Systemsadversarial gradient

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.

ClusteringRecommendation Systemsfeature 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+.

M5 modelMulti-modalRecommendation 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.

Model UpdateRecommendation SystemsWeChat

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.

ALSCollaborative FilteringRecommendation Systems

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 ServiceRecommendation Systems

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 NetworkIndustry Case Study

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.

AIRecommendation Systemsmachine 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.

Domain AdaptationRecommendation Systemscontrastive learning

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.

AIAlgorithm EngineeringRanking

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.

AIBias MitigationKuaishou

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 EmbeddingKnowledge GraphRecommendation Systems

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.

EmbeddingRecommendation Systemsmultimodal learning

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.

CalibrationOnline AdvertisingRecommendation Systems

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 SystemsUser Modeling

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 Systemsmulti-content mixing

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.

AIKuaishouRecommendation Systems

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.

EmbeddingItem2VecRecommendation Systems

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 MechanismKnowledge GraphRecommendation 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.

Knowledge GraphRecommendation Systemsattention

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.

Large‑Scale TrainingPGL2.2Recommendation Systems

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 IntelligenceRecommendation SystemsTencent Music

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.

Recommendation Systemsdeep learningfactorization 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.

AlgorithmRecommendation SystemsRetrieval

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 SystemsSelf-supervised LearningTransfer 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.

EM algorithmRecommendation Systemsbeam search

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 testingRecommendation Systemscausal inference

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 SystemsSelf-supervised Learningcontrastive learning

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 IntelligenceKnowledge GraphRecommendation Systems

0 likes · 4 min read

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