mlsanityData to 14 Oct 2026

Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights

arXiv 2305.11700 ↗First posted 19 May 2023cs.IRcited by 38

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Abstract (arXiv)

Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent items. However, the current landscape of TCF models mainly relies on the utilization of relatively small or medium-sized LMs. The potential impact of using larger, more powerful language models (such as these with over 100 billion parameters) as item encoders on recommendation performance remains uncertain. Can we anticipate unprecedented results and discover new insights? To address this question, we undertake a comprehensive series of experiments aimed at exploring the performance limits of the TCF paradigm. Specifically, we progressively augment the scale of item encoders, ranging fromone hundred million to one hundred billion parameters, in order to reveal the scaling limits of the TCF paradigm. Moreover, we investigate whether these exceptionally large LMs have the potential to establish a universal item representation for the recommendation task, thereby revolutionizing the traditional ID paradigm, which is considered a significant obstacle to developing transferable "one model fits all" recommender models. Our study not only demonstrates positive results but also uncovers unexpected negative outcomes, illuminating the current state of the TCF paradigm within the community. These findings will evoke deep reflection and inspire further research on text-based recommender systems.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 May 2023.

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Citing papers per year: 2023: 10; 2024: 14; 2025: 12; 2026: 2.

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  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  2. 2023A Survey on Large Language Models for Recommendationcited by 379
  3. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  4. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  6. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  7. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  8. 2024User-LLM: Efficient LLM Contextualization with User Embeddingscited by 76
  9. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  10. 2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
  11. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  12. 2024LLMs are Also Effective Embedding Models: An In-depth Overviewcited by 32
26 more
  1. 2024Rethinking Large Language Model Architectures for Sequential Recommendationscited by 27
  2. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  3. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
  4. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 17
  5. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  6. 2023LKPNR: LLM and KG for Personalized News Recommendation Frameworkcited by 13
  7. 2024RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMscited by 13
  8. 2024SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber Worldcited by 10
  9. 2025Advancing and Benchmarking Personalized Tool Invocation for LLMscited by 8
  10. 2024Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendationcited by 7
  11. 2024PAP-REC: Personalized Automatic Prompt for Recommendation Language Modelcited by 4
  12. 2025Large Language Model as Universal Retriever in Industrial-Scale Recommender Systemcited by 4
  13. 2024Large Language Models Make Sample-Efficient Recommender Systemscited by 3
  14. 2024MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommendercited by 2
  15. 2024Enhancing ID-based Recommendation with Large Language Modelscited by 2
  16. 2024Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendationcited by 1
  17. 2025JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecturecited by 1
  18. 2025Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendationcited by 1
  19. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1
  20. 2025Enhance Large Language Models as Recommendation Systems with Collaborative Filteringcited by 1
  21. 2026Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendationscited by 1
  22. 2025Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 0
  23. 2025Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferencescited by 0
  24. 2025ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Modelscited by 0
  25. 2025Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendationcited by 0
  26. 2026GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigmcited by 0