mlsanityData to 7 Oct 2026

Recommender Systems with Generative Retrieval

arXiv 2305.05065 ↗First posted 8 May 2023cs.IR cs.LGcited by 191

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Abstract (arXiv)

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.

Key facts. Cited by 191 papers in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

35 of 59 references.

References · 35

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2017Neural Discrete Representation Learningcited by 4,650
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  6. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  7. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  8. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  9. 2018Self-Attentive Sequential Recommendationcited by 1,169
  10. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  11. 2021SoundStream: An End-to-End Neural Audio Codeccited by 756
  12. 2016Wide & Deep Learning for Recommender Systemscited by 745
23 more
  1. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  2. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  3. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  4. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  5. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 473
  6. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  7. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  8. 2020Autoregressive Entity Retrievalcited by 333
  9. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  10. 2022Transformer Memory as a Differentiable Search Indexcited by 242
  11. 2017Neural Attentive Session-based Recommendationcited by 237
  12. 2017The Case for Learned Index Structurescited by 188
  13. 2022Scaling Up Models and Data with textttt5x and textttseqiocited by 187
  14. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  15. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  16. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  17. 2022A Neural Corpus Indexer for Document Retrievalcited by 107
  18. 2019Hierarchical Gating Networks for Sequential Recommendationcited by 101
  19. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 89
  20. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  21. 2020Learning to Embed Categorical Features without Embedding Tables for Recommendationcited by 38
  22. 2022TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedbackcited by 27
  23. 2018Next Item Recommendation with Self-Attentioncited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 27; 2025: 61; 2026: 96.

191 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023A Survey on Large Language Models for Recommendationcited by 378
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  4. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  5. 2026Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Modelscited by 78
  6. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  7. 2023Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendationscited by 50
  8. 2023How Does Generative Retrieval Scale to Millions of Passages?cited by 47
  9. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  10. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  11. 2024Rethinking Large Language Model Architectures for Sequential Recommendationscited by 27
  12. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
38 more
  1. 2023Language Models As Semantic Indexerscited by 20
  2. 2023Embedding in Recommender Systems: A Surveycited by 19
  3. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  4. 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendationcited by 18
  5. 2025RecoWorld: Building Simulated Environments for Agentic Recommender Systemscited by 18
  6. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  7. 2024Unifying Generative and Dense Retrieval for Sequential Recommendationcited by 16
  8. 2025MiniOneRec: An Open-Source Framework for Scaling Generative Recommendationcited by 16
  9. 2025Multimodal Medical Code Tokenizercited by 14
  10. 2025Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrievalcited by 13
  11. 2024AesopAgent: Agent-driven Evolutionary System on Story-to-Video Productioncited by 12
  12. 2025Multimodal Quantitative Language for Generative Recommendationcited by 12
  13. 2024Efficient Inference for Large Language Model-based Generative Recommendationcited by 11
  14. 2024Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Modelcited by 11
  15. 2025Slow Thinking for Sequential Recommendationcited by 11
  16. 2026GR2: Generative Reasoning Re-rankercited by 11
  17. 2024End-to-end Learnable Clustering for Intent Learning in Recommendationcited by 10
  18. 2024Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningcited by 10
  19. 2025Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Modelscited by 10
  20. 2025MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptationcited by 10
  21. 2025CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendationcited by 10
  22. 2026NextMem: Towards Latent Factual Memory for LLM-based Agentscited by 10
  23. 2024A Generative Approach for Wikipedia-Scale Visual Entity Recognitioncited by 9
  24. 2024Preference Diffusion for Recommendationcited by 8
  25. 2025PinRec: Unified Generative Retrieval for Pinterest Recommender Systemscited by 8
  26. 2025Diffusion Generative Recommendation with Continuous Tokenscited by 8
  27. 2024Item-Language Model for Conversational Recommendationcited by 7
  28. 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Surveycited by 7
  29. 2025FreeRet: MLLMs as Training-Free Retrieverscited by 7
  30. 2024Preference Discerning with LLM-Enhanced Generative Retrievalcited by 6
  31. 2025RecLLM-R1: A Two-Stage Training Paradigm with Reinforcement Learning and Chain-of-Thought v1cited by 6
  32. 2025Understanding Generative Recommendation with Semantic IDs from a Model-scaling Viewcited by 6
  33. 2026OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Frameworkcited by 6
  34. 2024Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learningcited by 5
  35. 2024Web-Scale Visual Entity Recognition: An LLM-Driven Data Approachcited by 5
  36. 2025GENIUS: A Generative Framework for Universal Multimodal Searchcited by 5
  37. 2025Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Modelscited by 5
  38. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5

50 most cited of 191 All 191 (JSON)