Tagged articles

recommendation systems

502 articles · Page 4 of 6

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.

cold startheterogeneous graph neural networkkeyword recommendation

0 likes · 14 min read

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

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.

algorithmdeep learninggraph indexing

0 likes · 28 min read

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

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.

PretrainingSelf-Supervised LearningUser Representation

0 likes · 11 min read

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

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 TrainingPerformance OptimizationSparse Parameters

0 likes · 31 min read

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

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.

adaptive maskingdimension selectionembedding

0 likes · 15 min read

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

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 algorithmbeam searchdeep retrieval

0 likes · 11 min read

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

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 testingCausal InferenceData Science

0 likes · 20 min read

Practical Insights into Online Experiment Design and Analysis at Tencent Lookpoint

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 optimizatione-commercemachine learning

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

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 testingCausal Inferenceexperiment analysis

0 likes · 21 min read

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

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.

Contrastive LearningInfoNCEdual-tower models

0 likes · 40 min read

Contrastive Learning Perspectives on Retrieval and Ranking Models in Recommendation Systems

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.

Contrastive LearningGraph Neural NetworksSelf-Supervised 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 IntelligenceNatural Language Processingknowledge graph

0 likes · 4 min read

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

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.

GPU accelerationbeam searchmodel 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

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 IntelligenceGraph Neural NetworksInformation Retrieval

0 likes · 25 min read

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

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

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 InterestOnline Advertising

0 likes · 17 min read

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

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.

Search Optimizationbudget optimizatione-commerce

0 likes · 20 min read

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

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.

Time Serieschurn predictionend-to-end models

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.

Meituanmachine learningmulti-task learning

0 likes · 15 min read

Multi-Business Ranking Modeling in Meituan Search

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 efficiencyReinforcement Learning

0 likes · 12 min read

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

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.

BVSHGJD.comLarge-Scale Graph

0 likes · 17 min read

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

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 NetworkPretrainingcross 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 ColumnsPerformance OptimizationTensorFlow

0 likes · 11 min read

Performance Optimization of TensorFlow Feature Columns in Recommendation Systems

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.

Bias MitigationTransformerheterogeneous graph

0 likes · 22 min read

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

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.

KDDSparse DataWorkshop

0 likes · 6 min read

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

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.

AttentionSequence ModelingTransformer

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

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.

Graph Neural NetworkMomoembedding

0 likes · 19 min read

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

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.

AITaobao Livedeep learning

0 likes · 27 min read

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

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.

AICTR predictionLong-Term Interest

0 likes · 16 min read

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

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.

MTLNLPmodel sharing

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.

Meta-Learningedge computingmobile AI

0 likes · 19 min read

EdgeRec: Edge Computing in Recommendation Systems

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.

AIdeep learningfeature engineering

0 likes · 20 min read

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

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 learningfeature engineeringmodel optimization

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 2021causal learningcomputer vision

0 likes · 12 min read

How JD Tech’s Breakthrough AI Papers Dominated AAAI 2021

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.

AlibabaFliggyinteractive recommendation

0 likes · 17 min read

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

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.

Hulucontent embeddinggraph embeddings

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.

Recallcollaborative filteringdeep learning

0 likes · 13 min read

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

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.

BeikeLSTMdeep learning

0 likes · 19 min read

User Preference Mining and Modeling Practices at Beike

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 Intelligencegraph embeddingknowledge graph

0 likes · 12 min read

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

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 IntelligenceGraph Neural NetworksGraphSAGE

0 likes · 10 min read

Introduction to Graph Neural Networks and Their Applications in Recommendation Systems

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.

DGIGraphSAGETencent

0 likes · 8 min read

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

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.

AIdual-tower modelmulti-modal features

0 likes · 9 min read

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

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.

AIcollaborative filteringdeepfm

0 likes · 9 min read

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

Dec 17, 2020 · Artificial Intelligence

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

The article presents a comprehensive study of re‑ranking in large‑scale industrial recommendation pipelines, identifies four key challenges—context awareness, permutation specificity, computational complexity, and business constraints—and proposes a two‑stage List Retrieval System that combines fast sequence search and a generative re‑ranking network with a deep context‑wise model, achieving significant online gains across multiple Taobao feed scenarios.

Context-Awaredeep learningindustrial AI

0 likes · 28 min read

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

Ctrip Technology

Dec 10, 2020 · Artificial Intelligence

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

This article presents a comprehensive study on automatically extracting theme‑based recommendation reasons for travel content, detailing a three‑stage retrieval framework, the advantages of interactive matching models over classification, rule‑based and back‑translation data augmentation techniques, and various model optimization strategies including priors, transfer learning, seed selection, optimizer choice, and layer‑wise learning rates.

AIData AugmentationNatural Language Processing

0 likes · 19 min read

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

Meituan Technology Team

Dec 3, 2020 · Artificial Intelligence

Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020

Meituan’s search and NLP team announced that six knowledge‑graph papers—covering query‑aware tip generation, BERT‑based ranking, multi‑modal and sequential recommendation, conversational recommendation, and graph‑embedding for personalized product search—were accepted at CIKM 2020, resulting from university collaborations and already deployed to boost Meituan’s search, recommendation and product‑search services.

BERTCIKM 2020Multi-modal

0 likes · 13 min read

Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020

Dec 2, 2020 · Artificial Intelligence

How Recommendation Algorithms Drive User Growth in Content Feed Systems

This article examines how low‑level recommendation algorithm techniques can upgrade content‑feed systems to boost user growth, covering problem analysis, growth factors, personalization upgrades, cold‑start mechanisms, bias mitigation via causal inference, and utility‑driven user profiling.

Causal Inferencealgorithm designcontent feed

0 likes · 14 min read

How Recommendation Algorithms Drive User Growth in Content Feed Systems

Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

Graph Neural NetworksNatural Language Processingembedding

0 likes · 32 min read

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

Nov 24, 2020 · Artificial Intelligence

Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms

This article examines the unique properties of novel literature, the difficulties of tag‑based representation, and how multi‑modal AI techniques—including dual‑tower models, feature fusion, clustering, and YouTube‑style DNN recall—are applied to improve recommendation accuracy and user decision‑making.

YouTube DNNdual-tower modelmulti-modal AI

0 likes · 7 min read

Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms

Nov 12, 2020 · Artificial Intelligence

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

This article explains how reinforcement learning, with its focus on maximizing long‑term reward, can improve recommendation system mixing by covering basic RL concepts, differences from supervised learning, multi‑armed bandit approaches, practical OpenAI Gym experiments, new AUC metrics, online gains, and advanced model optimizations.

Artificial IntelligenceOpenAI GymQ-Learning

0 likes · 10 min read

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

Bitu Technology

Nov 10, 2020 · Artificial Intelligence

Key Takeaways from RecSys 2020: Conference Organization and Notable Research Highlights

The article reviews RecSys 2020’s shift to a virtual format, highlights the organizers’ use of tools like Whova and Gather.town, and summarizes several industrial and academic research breakthroughs presented at the conference, including PURS, behavior‑based popularity ranking, contextual item‑to‑item recommendation, counterfactual learning, debiasing techniques, and a large‑scale bandit dataset.

Academic PapersArtificial IntelligenceIndustrial Research

0 likes · 9 min read

Key Takeaways from RecSys 2020: Conference Organization and Notable Research Highlights

Yuewen Technology

Nov 10, 2020 · Artificial Intelligence

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.

Data AnalysisLambdaMARTLightGBM

0 likes · 9 min read

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

JD Cloud Developers

Oct 29, 2020 · Artificial Intelligence

How JD Leverages Knowledge Graphs for Better E‑commerce Interest Recall

JD’s recommendation team outlines three key innovations—knowledge‑graph‑based interest recall, enhanced CTR estimation with a DRM module, and a listwise ranking strategy—that together address user‑interest expansion challenges in e‑commerce, especially for cold‑start items, long‑tail products, and dynamic promotional scenarios.

CTR estimatione-commerceinterest expansion

0 likes · 21 min read

How JD Leverages Knowledge Graphs for Better E‑commerce Interest Recall

Oct 26, 2020 · Artificial Intelligence

Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings

The Hulu AI Class event showcased a series of technical talks covering large‑scale graph neural network optimizations, multi‑factor video ad placement algorithms, recommendation and search engine techniques, machine‑learning‑driven video codec improvements, and advanced content‑embedding methods, highlighting practical engineering experiences from Hulu’s Beijing office.

Ad Targetingcontent embeddingmachine learning

0 likes · 9 min read

Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings

360 Zhihui Cloud Developer

Sep 16, 2020 · Artificial Intelligence

How TensorNet Supercharges Sparse Feature Training on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework optimized for massive sparse‑feature models in advertising and recommendation, dramatically reducing parameter sync overhead, enabling near‑infinite feature dimensions, cutting training time from hours to minutes, and boosting inference performance by up to 35%.

AITensorFlowTensorNet

0 likes · 10 min read

How TensorNet Supercharges Sparse Feature Training on TensorFlow

Sep 15, 2020 · Artificial Intelligence

How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights

This article explores the fundamentals and evaluation of recommendation systems, detailing their definition, key performance dimensions such as accuracy, diversity, novelty, serendipity, trust, and real‑time utility, and presents a practical case study from 58.com with reflections on methodology and future improvements.

Artificial Intelligenceevaluation metricspersonalization

0 likes · 12 min read

How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights

Aug 30, 2020 · Big Data

Large-Scale Recommendation System Feature Engineering and Optimization with Spark and FESQL

This article explains how large-scale recommendation systems rely on efficient feature engineering, describes the three-layer architecture (offline, stream, online), and details how Spark SQL and the LLVM‑optimized FESQL engine improve performance and ensure offline‑online feature consistency.

FESQLLLVMSQL optimization

0 likes · 13 min read

Large-Scale Recommendation System Feature Engineering and Optimization with Spark and FESQL

Aug 27, 2020 · Artificial Intelligence

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

This article explains the fundamental differences between computational advertising and recommendation systems, outlines the distinct problems each field addresses, and surveys the most widely used advertising models—including traditional machine‑learning approaches, deep‑learning architectures, and hybrid solutions—providing practical insights for engineers in both domains.

AICTR modelscomputational advertising

0 likes · 11 min read

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

Aug 27, 2020 · Artificial Intelligence

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

This article explains Alibaba’s User Interest Center approach to real‑time model serving, detailing how it separates offline sequence modeling from lightweight online inference, uses an online interest‑embedding store, and dramatically reduces latency for recommendation models such as DIEN and MIMN.

Alibabaembeddingmodel serving

0 likes · 8 min read

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

Meituan Technology Team

Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingBias MitigationGraph Modeling

0 likes · 23 min read

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

Jul 31, 2020 · Artificial Intelligence

WeChat 'Kan Kan' Content Understanding: Architecture and Techniques for Recommendation

This article details the technical architecture behind WeChat's 'Kan Kan' content understanding platform, covering text and multimedia analysis, tag extraction, entity recognition, knowledge graph construction, and how these components enhance recommendation recall, ranking, and user engagement across the ecosystem.

content understandingknowledge graphmachine learning

0 likes · 46 min read

WeChat 'Kan Kan' Content Understanding: Architecture and Techniques for Recommendation

Jul 25, 2020 · Backend Development

How SimSvr Achieves Billion‑Scale Real‑Time ANN Search for Recommendations

SimSvr is a high‑performance, distributed feature‑retrieval component designed for recommendation systems that supports billion‑scale indexes, sub‑millisecond query latency, real‑time and batch updates, multi‑model AB‑testing, and advanced filtering, all while running on Tencent's production workloads.

ANNfeature retrievalreal-time indexing

0 likes · 17 min read

How SimSvr Achieves Billion‑Scale Real‑Time ANN Search for Recommendations

Meituan Technology Team

Jul 23, 2020 · Artificial Intelligence

43rd ACM SIGIR 2020 Conference Overview

The 43rd ACM SIGIR 2020 International Conference on Research and Development in Information Retrieval, a premier CCF A‑class event, will be held virtually from July 25‑30, featuring theoretical and applied research on knowledge graph construction, explainable recommendation, and content generation, with a keynote by Meituan Waimai’s senior algorithm expert Maodi Hu.

ACM SIGIRAI applicationsknowledge graph

0 likes · 4 min read

43rd ACM SIGIR 2020 Conference Overview

Jul 19, 2020 · Product Management

Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms

This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.

Data AnalysisProduct Strategymatching algorithms

0 likes · 30 min read

Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms

Jul 16, 2020 · Operations

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

The paper describes Didi’s online driver‑repositioning system that treats idle‑driver dispatch as a recommender problem, generating candidate destinations, scoring tasks with a marginal‑gain model, and selecting optimal assignments via a minimum‑cost‑flow optimizer, which in live A/B tests boosted driver efficiency, earnings, and satisfaction while reducing empty cruising.

AB Testingdriver repositioningfleet management

0 likes · 11 min read

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

Jul 12, 2020 · Artificial Intelligence

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

This article surveys the role of social tagging (folksonomy) in modern recommendation systems, describing how user‑generated tags form a three‑dimensional "tag cube" that can be combined with rating matrices, and reviewing a range of algorithms—including neighbor‑based, ranking (FolkRank/SocialRank), content‑based, linear regression, and matrix‑factorization approaches—while also discussing tag selection, noise handling, and scalability challenges.

AIcollaborative filteringfolksonomy

0 likes · 35 min read

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

Sohu Tech Products

Jul 8, 2020 · Artificial Intelligence

Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms

This article explains the fundamentals of recommendation systems, discusses early hotness‑based approaches, describes modern architectures with recall and ranking stages, reviews collaborative‑filtering techniques, matrix factorization, deep learning models such as NCF and NeuMF, and details how these methods are applied and optimized for live‑streaming services.

AIcollaborative filteringdeep learning

0 likes · 30 min read

Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms

Jun 27, 2020 · Artificial Intelligence

What AI Brings to Financial Investment: Limitations of Recommendation Models Compared to Live‑Streaming Commerce

The article examines the rapid growth of live‑streaming e‑commerce, explains the trust‑based dynamics of influencers, outlines standard recommendation‑system metrics such as accuracy, recall, diversity and explainability, and argues that these models fall short of long‑term user utility because they are driven by short‑term commercial goals, highlighting economic and neuroscientific perspectives on preference randomness.

Artificial Intelligenceeconomicsfinancial investment

0 likes · 13 min read

What AI Brings to Financial Investment: Limitations of Recommendation Models Compared to Live‑Streaming Commerce

Jun 21, 2020 · Artificial Intelligence

Comprehensive Guide to Recommendation Engine Types and Techniques

This article provides a detailed overview of various recommendation system types—including neighbor-based, personalized, content-based, contextual, hybrid, and model-based approaches—explaining their principles, advantages, disadvantages, and practical examples with formulas and visual illustrations for real-world applications.

Context-Awarecollaborative filteringcontent-based

0 likes · 28 min read

Comprehensive Guide to Recommendation Engine Types and Techniques

iQIYI Technical Product Team

Jun 19, 2020 · Artificial Intelligence

From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey

iQIYI’s recommendation team transformed its offline, slow‑query system into a real‑time engine by sharding databases, adding caching, and adopting Kafka, Spark‑Streaming and Flink, cutting peak timeout from 4% to under 0.3%, delivering second‑level personalized, diverse, high‑quality video suggestions while keeping engineers close to the front‑line.

iQIYImachine learningpersonalization

0 likes · 7 min read

From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey

NetEase Media Technology Team

Jun 12, 2020 · Artificial Intelligence

Semantic Text Understanding for NetEase News Feed Recommendation

NetEase improves its news‑feed recommendation by applying a multi‑stage semantic text understanding pipeline—lexical analysis, hierarchical content tagging, and quality filtering—using two‑level classifiers, LDA‑based topic modeling, multi‑label concept and entity extraction, and dense vector representations to better capture user interests and boost personalization performance.

NLPfeature engineeringmachine learning

0 likes · 9 min read

Semantic Text Understanding for NetEase News Feed Recommendation

Jun 4, 2020 · Artificial Intelligence

Exploring Federated Recommendation Algorithms and Their Applications

This article introduces the challenges of traditional centralized recommendation systems, explains the principles and implementations of federated recommendation algorithms—including vertical and horizontal federated matrix factorization and factorization machines—using WeBank’s open-source FATE platform, and discusses cloud services, practical use cases, and performance benefits.

AIFATEfederated learning

0 likes · 13 min read

Exploring Federated Recommendation Algorithms and Their Applications

Sohu Tech Products

May 27, 2020 · Artificial Intelligence

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

This article provides a comprehensive overview of graph embedding methods—including DeepWalk, LINE, node2vec, and EGES—explaining their algorithms, random‑walk strategies, proximity definitions, incorporation of side information, and their applications in large‑scale recommendation systems.

DeepWalkgraph embeddingline

0 likes · 20 min read

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

May 26, 2020 · Artificial Intelligence

Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights

This article reviews how knowledge distillation—using a large teacher model to guide a smaller student model—can be applied across the recall, coarse‑ranking, and fine‑ranking stages of recommendation systems, detailing logits‑based and feature‑based approaches, joint and two‑stage training, and point‑wise, pair‑wise, and list‑wise loss designs.

knowledge distillationmachine learningmodel compression

0 likes · 31 min read

Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights

May 23, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Based on academic and industry experience, iQIYI has designed a deep semantic representation learning framework that integrates multimodal side information and deep models such as Transformers and graph neural networks, improving recall, ranking, deduplication, diversity and semantic matching across recommendation and search scenarios.

Graph Neural NetworksMultimodaldeep learning

0 likes · 27 min read

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Alibaba Cloud Developer

May 21, 2020 · Artificial Intelligence

How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals

This article examines the DeepMatch retrieval model for Tmall Genie music recommendation, detailing how incorporating user feedback such as play‑rate and query intent signals via multi‑task learning and feedback‑aware self‑attention improves recall accuracy and reduces negative recommendations, while also discussing embedding factorization, loss functions, and distributed training optimizations.

deep learningmultitask learningrecommendation systems

0 likes · 18 min read

How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals

May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictionReinforcement Learningdeep learning

0 likes · 10 min read

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

JD Retail Technology

May 13, 2020 · Artificial Intelligence

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

JD announced that two of its research papers—one on a hierarchical reinforcement‑learning framework for multi‑objective recommendation (MaHRL) and another on an attention‑based model for delayed‑feedback conversion‑rate prediction (pCVR)—were accepted as full papers at the prestigious IJCAI2020 and SIGIR2020 conferences, highlighting the company's strong AI capabilities.

Artificial IntelligenceReinforcement Learningconversion rate prediction

0 likes · 6 min read

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

May 8, 2020 · Artificial Intelligence

Distributed Machine Learning Framework GDBT for High‑Dimensional Real‑Time Recommendation Systems

The article explains how the fourth paradigm's distributed machine learning framework GDBT tackles the massive data, high‑dimensional features, and real‑time requirements of modern recommendation systems by leveraging heterogeneous computing, parameter servers, RDMA networking, and optimized workloads.

GDBTRDMAdistributed ML

0 likes · 18 min read

Distributed Machine Learning Framework GDBT for High‑Dimensional Real‑Time Recommendation Systems

Apr 27, 2020 · Artificial Intelligence

Graph-Based Recommendation Algorithms and Cold‑Start Solutions

This article presents a comprehensive overview of graph‑based recommendation techniques, including collaborative filtering, graph embedding, side‑information enhanced embeddings, two‑tower DSSM models, and practical cold‑start strategies from Alibaba and Airbnb, followed by a mixed model and Q&A session.

AIcold startcollaborative filtering

0 likes · 14 min read

Graph-Based Recommendation Algorithms and Cold‑Start Solutions

Apr 23, 2020 · Artificial Intelligence

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

The article explains how Alibaba Entertainment leverages causal inference and uplift modeling to build unbiased user‑cf recommendation algorithms that model user states and upgrade personalized distribution, achieving significant click‑through and re‑activation gains for long‑video services like Youku.

Causal InferenceVideo platformpersonalization

0 likes · 13 min read

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

Apr 21, 2020 · Artificial Intelligence

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

This article surveys the application of attention mechanisms in deep learning recommendation systems, reviewing models such as AFM, DIN, DIEN, DSIN, Behavior Sequence Transformer, Deep Spatio‑Temporal Networks, and ATRank, and discusses their architectures, attention types, advantages, and limitations.

AttentionCTR predictiondeep learning

0 likes · 10 min read

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Graph Neural NetworkReinforcement Learningdeep learning

0 likes · 14 min read

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

Apr 6, 2020 · Artificial Intelligence

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

DeepMatch is an open‑source Python library that implements several mainstream deep‑learning based recall‑matching algorithms, provides easy installation via pip, detailed usage examples with code, and supports exporting user and item vectors for ANN search, making it ideal for rapid experimentation and learning in recommendation systems.

ANNPythonVector Retrieval

0 likes · 10 min read

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

Mar 13, 2020 · Artificial Intelligence

Knowledge Graph Assisted Personalized Recommendation Systems

Personalized recommendation systems, essential for modern internet platforms, can be enhanced by knowledge graphs which provide auxiliary information to improve accuracy, diversity, and explainability, with various methods such as embedding-based (DKN, MKR), path-based, and hybrid approaches like RippleNet and KGCN.

KG-awarecollaborative filteringknowledge graph

0 likes · 21 min read

Knowledge Graph Assisted Personalized Recommendation Systems

Mar 11, 2020 · Artificial Intelligence

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive technical overview of Toutiao’s recommendation system, covering its three‑dimensional modeling approach, feature engineering, user‑tag pipelines, real‑time training infrastructure, evaluation methodology, and content‑safety mechanisms.

A/B testingReal-time Trainingcontent safety

0 likes · 17 min read

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

Mar 9, 2020 · Artificial Intelligence

Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds

ByteDance’s recommendation system models user satisfaction as a function of content, user, and context features, employing diverse algorithms—from logistic regression to deep learning—while leveraging real‑time training, hierarchical text classification, dynamic user tagging, rigorous A/B testing, and multi‑layer content safety checks to deliver personalized feeds at massive scale.

Real-time TrainingUser Taggingcontent safety

0 likes · 19 min read

Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds

Mar 9, 2020 · Artificial Intelligence

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.

deep learningknowledge graphmultimodal AI

0 likes · 6 min read

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

Qunar Tech Salon

Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR predictiondeep learningmatching

0 likes · 12 min read

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

Tianxing Digital Tech User Experience

Feb 28, 2020 · Fundamentals

How User Guidance Boosts Product Experience: Design Principles and Strategies

This article explains how various user‑guidance techniques—layout modularity, middle‑option bias, task‑based prompts, rebate incentives, herd‑mentalities, and behavior‑driven recommendations—can steer decisions, accelerate conversions, and enhance overall product experience.

Goldilocks principleproduct designrecommendation systems

0 likes · 6 min read

How User Guidance Boosts Product Experience: Design Principles and Strategies

Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Ranking Modelsdeep learningknowledge distillation

0 likes · 13 min read

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

Jan 21, 2020 · Artificial Intelligence

How to Enhance Real-Time Updating of Recommendation System Models

The article examines various techniques—including full, incremental, online, and local updates—as well as client‑side embedding refreshes to improve the real‑time performance of recommendation system models, balancing freshness with global optimality.

AIincremental learningmachine learning

0 likes · 9 min read

How to Enhance Real-Time Updating of Recommendation System Models

Huajiao Technology

Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

This article reviews the evolution of ranking models in modern recommendation systems, covering traditional linear models, factorization machines, tree‑based GBDT+LR, and a range of deep learning architectures such as Wide&Deep, DeepFM, DCN, xDeepFM, DIN, as well as multi‑task frameworks like ESMM and MMOE, and finally illustrates their practical deployment in a live streaming platform.

deep learningfeature engineeringmachine learning

0 likes · 20 min read

Overview of Ranking Algorithms in Recommendation Systems

Jan 6, 2020 · Artificial Intelligence

Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling

The article explains Weibo’s O‑Series advertising system, detailing its three‑part strategy of smart bidding, intelligent targeting, and ROI modeling, the underlying machine‑learning techniques such as deep‑FM, dual‑tower and PID control, and how these components optimize show, click, conversion rates and advertiser ROI.

AdvertisingROImachine learning

0 likes · 14 min read

Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling

Dec 30, 2019 · Artificial Intelligence

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

This article surveys recent advances in recommendation system technology, covering the evolution from a two‑stage recall‑ranking pipeline to a four‑stage architecture, and detailing emerging trends in model‑based recall, user‑behavior sequence modeling, knowledge‑graph integration, graph neural networks, advanced ranking models, multi‑objective optimization, multimodal fusion, and listwise re‑ranking.

Graph Neural NetworksInformation Retrievalknowledge graph

0 likes · 45 min read

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

Dec 30, 2019 · Artificial Intelligence

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications

Recent industrial advances in multi-task learning for recommendation systems, including Alibaba's ESMM and DUPN, Meituan's deep ranking, Google's MMoE, YouTube's multi-objective ranking, and Zhihu's multi-goal model, demonstrate how shared embeddings and specialized loss functions improve CTR, CVR, and user engagement metrics.

AICTRCVR

0 likes · 14 min read

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications