recommender systems

Zhuanzhuan Tech

Sep 11, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments

This article introduces causal inference concepts, explains structural causal and potential‑outcome frameworks, presents the MACR model for debiasing popularity in recommender systems, and details two experiments conducted on the ZhaiZhai platform along with future research directions.

MACRcausal inferencecounterfactual reasoning

0 likes · 13 min read

Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments

DataFunSummit

Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 24 min read

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

Sohu Tech Products

Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

DataFunTalk

Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

Bias Mitigationcausal inferenceinterest disentanglement

0 likes · 23 min read

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

DataFunSummit

Dec 15, 2023 · Artificial Intelligence

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

This article explores how large language models can be incorporated into recommender systems, discussing background challenges, specific integration points across the recommendation pipeline, practical implementation methods, experimental results, and future research directions, while highlighting industrial considerations and potential improvements.

LLMModel Fusionfeature engineering

0 likes · 20 min read

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

Kuaishou Tech

Nov 23, 2023 · Artificial Intelligence

KuaiSim: A Comprehensive User Simulator for Reinforcement Learning in Recommendation Systems

KuaiSim is a comprehensive user simulation environment for recommendation systems that models immediate, long‑term, and cross‑session feedback, supports list‑wise, whole‑session, and retention tasks, provides baselines and evaluation metrics, and demonstrates superior performance on KuaiRand and ML‑1M datasets.

KuaiSimUser Simulationbenchmark

0 likes · 14 min read

KuaiSim: A Comprehensive User Simulator for Reinforcement Learning in Recommendation Systems

NetEase Media Technology Team

Nov 6, 2023 · Artificial Intelligence

Overview of Sequential Recommendation Models

The article surveys sequential recommendation models from early non-deep approaches like FPMC, through RNN-based GRU4Rec and CNN-based Caser, to Transformer-based methods such as SASRec, BERT4Rec, TiSASRec, and recent contrastive-learning techniques, recommending SASRec or its variants for production use.

Deep Learningcontrastive learningrecommender systems

0 likes · 17 min read

Overview of Sequential Recommendation Models

DataFunTalk

Oct 10, 2023 · Artificial Intelligence

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

This article surveys how large language models can be incorporated into recommender systems, discussing their strengths and limitations, outlining where and how they can be applied across the recommendation pipeline, presenting recent research examples, and highlighting challenges and future directions for industrial deployment.

LLMfeature engineeringlarge language models

0 likes · 20 min read

DataFunSummit

Aug 18, 2023 · Artificial Intelligence

Survey of Recent Generative AI Approaches for Recommender Systems

This article reviews a series of recent research papers that explore how generative AI, especially large language models, can be integrated into recommender systems to improve next‑basket recommendation, instruction‑following, zero‑shot ranking, fairness evaluation, and generative retrieval paradigms.

Generative AIfairnesslarge language models

0 likes · 12 min read

Survey of Recent Generative AI Approaches for Recommender Systems

Kuaishou Tech

Aug 7, 2023 · Artificial Intelligence

GFN4Rec: Generative Flow Networks for Listwise Recommendation

This paper introduces GFN4Rec, a generative flow network approach for listwise recommendation that models the entire list generation as a probability flow, optimizing list-level reward to simultaneously improve recommendation accuracy and diversity, and validates its effectiveness on multiple datasets and simulators.

GFlowNetGenerative Modelsai

0 likes · 8 min read

GFN4Rec: Generative Flow Networks for Listwise Recommendation

DaTaobao Tech

Jun 9, 2023 · Artificial Intelligence

Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow

The paper introduces a Generator‑Evaluator (GE) architecture that end‑to‑end re‑ranks information‑flow items using a pointer‑network seq2seq generator and a reward‑estimating evaluator, jointly optimizing relevance and business utilities such as diversity, traffic control, inter‑group ordering, and fixed‑slot insertion, achieving over 70% better‑percentage and significant online gains on Taobao.

Rankinggenerator-evaluatorinformation flow

0 likes · 19 min read

Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow

DataFunTalk

Jun 4, 2023 · Artificial Intelligence

Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems

This presentation introduces a decoupled domain‑adaptation network that separates popularity and attribute representations to mitigate popularity bias in recommender systems, describing the problem, existing IPS and causal‑inference solutions, the CD2AN architecture, experimental results, and practical Q&A.

aidomain adaptationmachine learning

0 likes · 13 min read

Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems

Kuaishou Tech

Apr 26, 2023 · Artificial Intelligence

Dual-Interest Decomposition Head Attention for Sequence Recommendation with Positive and Negative Feedback

The paper proposes a dual‑interest decomposition head‑attention model that uses a feedback‑aware encoding layer, a factorized head attention mechanism, and separate positive/negative interest towers to improve sequence recommendation performance on short‑video and e‑commerce datasets.

Feedbackairecommender systems

0 likes · 8 min read

Dual-Interest Decomposition Head Attention for Sequence Recommendation with Positive and Negative Feedback

DataFunSummit

Jul 20, 2022 · Artificial Intelligence

Explainable Recommendation: Background, Development History, Graph‑Based Structured Explanations, and Natural Language Generation

This article provides a comprehensive overview of explainable recommendation, covering its motivation, evolution, graph‑based structured explanation techniques, natural‑language generation methods, recent research advances, and open challenges such as fact‑checking, low‑resource scenarios, and evaluation metrics.

Knowledge GraphNatural Language Generationartificial intelligence

0 likes · 17 min read

Explainable Recommendation: Background, Development History, Graph‑Based Structured Explanations, and Natural Language Generation

NetEase LeiHuo UX Big Data Technology

Jun 30, 2022 · Artificial Intelligence

Personalized Recommendation of Game Cosmetic Items: From Popularity to Latent Factor Models

The article explores how to recommend visually appealing game cosmetics—such as character outfits and weapon skins—by transforming subjective notions of beauty into objective features using popularity heuristics, tag‑based labeling, and latent factor models to predict player preferences.

game cosmeticslatent factor modelpersonalization

0 likes · 8 min read

Personalized Recommendation of Game Cosmetic Items: From Popularity to Latent Factor Models

DataFunTalk

Jun 17, 2022 · Artificial Intelligence

Issues with Recommender System Benchmarks and Insights from the BARS Paper

This article examines the shortcomings of current recommender system benchmarks, explains why standardized datasets and metrics are essential, and highlights key findings from the recent BARS paper that propose a more open and reproducible benchmarking framework for recommendation research.

BARSaibenchmarking

0 likes · 6 min read

Issues with Recommender System Benchmarks and Insights from the BARS Paper

DataFunTalk

Jun 11, 2022 · Artificial Intelligence

Explainable Recommendation: Background, Development, Graph‑Based Structured Explanations, and Natural Language Generation Advances

This article reviews the emerging field of explainable recommendation, covering its motivation, historical evolution from template‑based to knowledge‑graph and generative‑language approaches, recent advances in graph‑structured and natural‑language explanations, key research works, industrial applications, and open challenges such as fact‑checking, low‑resource settings, and evaluation methods.

Knowledge GraphNatural Language Generationai

0 likes · 16 min read

Explainable Recommendation: Background, Development, Graph‑Based Structured Explanations, and Natural Language Generation Advances

Alimama Tech

May 11, 2022 · Artificial Intelligence

PICASSO: An Industrial-Scale Sparse Training Engine for Wide-and-Deep Recommender Systems

PICASSO, Alibaba’s GPU‑centric sparse training engine for wide‑and‑deep recommender systems, merges identical embedding tables, interleaves data and kernel operations, and caches hot embeddings on GPU, eliminating the parameter server and delivering up to tenfold speedups over TensorFlow‑PS while maintaining model quality.

AlibabaGPU optimizationSparse Training

0 likes · 14 min read

PICASSO: An Industrial-Scale Sparse Training Engine for Wide-and-Deep Recommender Systems

DataFunTalk

Apr 21, 2022 · Artificial Intelligence

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

This article explains why cold‑start is a critical challenge for recommender systems, outlines four practical strategies—generalization, fast data collection, transfer learning, and few‑shot learning—and then details the DropoutNet model, its end‑to‑end training, loss functions, negative‑sampling techniques, and open‑source implementation.

Cold StartDropoutNetembedding

0 likes · 21 min read

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

DaTaobao Tech

Apr 19, 2022 · Artificial Intelligence

Generative Re‑ranking for Diverse and Context‑Aware Recommendation

The paper presents a generative re‑ranking framework for Taobao’s home‑decor channel that combines heuristic sequence generation methods (MMR, DPP, beam search) with a context‑aware encoder to produce diverse, relevance‑balanced recommendation lists, achieving notable gains in PV, IPV, CTR and click‑diversity over traditional point‑wise ranking.

context-awarediversitygenerative re-ranking

0 likes · 19 min read

Generative Re‑ranking for Diverse and Context‑Aware Recommendation