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

arXiv: https://arxiv.org/abs/2305.11700 · HTML: https://mlsanity.com/paper/2305.11700/

## Key facts

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

## Built on by (most cited first, 30 of 38)

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- [Large Language Models for Generative Recommendation: A Survey and Visionary Discussions](https://mlsanity.com/paper/2309.01157/) (2023)
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- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
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- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Language Representations Can be What Recommenders Need: Findings and Potentials](https://mlsanity.com/paper/2407.05441/) (2024)
- [Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation](https://mlsanity.com/paper/2408.03533/) (2024)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [LKPNR: LLM and KG for Personalized News Recommendation Framework](https://mlsanity.com/paper/2308.12028/) (2023)
- [RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs](https://mlsanity.com/paper/2409.04421/) (2024)
- [SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World](https://mlsanity.com/paper/2412.07472/) (2024)
- [Advancing and Benchmarking Personalized Tool Invocation for LLMs](https://mlsanity.com/paper/2505.04072/) (2025)
- [Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation](https://mlsanity.com/paper/2411.02992/) (2024)
- [PAP-REC: Personalized Automatic Prompt for Recommendation Language Model](https://mlsanity.com/paper/2402.00284/) (2024)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Large Language Models Make Sample-Efficient Recommender Systems](https://mlsanity.com/paper/2406.02368/) (2024)
- [MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender](https://mlsanity.com/paper/2408.08686/) (2024)
- [Enhancing ID-based Recommendation with Large Language Models](https://mlsanity.com/paper/2411.02041/) (2024)
- [Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation](https://mlsanity.com/paper/2402.08921/) (2024)
- [JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture](https://mlsanity.com/paper/2504.10512/) (2025)
- [Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation](https://mlsanity.com/paper/2507.04623/) (2025)

## 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.