Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation

By Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-rong Wen. IEEE International Conference on Data Engineering, 2023

366 citations recorded by the source corpus.

Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there exists a large semantic gap between LLMs and recommender systems, since items to be recommended are often indexed by discrete identifiers (item ID) out of the LLM's vocabulary. In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. Our approach can directly generate items from the entire item set for recommendation, without relying on candidate items. Specifically, we make two major contributions in our approach. For item indexing, we design a learning-based vector quantization method with uniform semantic mapping, which can assign meaningful and non-conflicting IDs (called item indices) for items. For alignment tuning, we propose a series of specially designed tuning tasks to enhance the integration of collaborative semantics in LLMs. Our fine-tuning tasks enforce LLMs to deeply integrate language and collaborative semantics (characterized by the learned item indices), so as to achieve an effective adaptation to recommender systems. Extensive experiments demonstrate the effectiveness of our method, showing that our approach can outperform a number of competitive baselines including traditional recommenders and existing LLM-based recommenders. Our code is available at https://github.com/RUCAIBox/LC-Rec/.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9d2c9a7c3ff8f760b43fdd6a7082069a467c8956

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wayne Xin Zhao
2. Yupeng Hou
3. Hongyu Lu
4. Ruobing Xie
5. Junjie Zhang
6. Zihan Lin
7. Julian McAuley
8. Ji-rong Wen

In Information Systems

1. Yupeng Hou
2. Wayne Xin Zhao
3. Hongyu Lu
4. Zihan Lin
5. Ruobing Xie
6. Junjie Zhang
7. Julian McAuley
8. Ji-rong Wen

Citations in the corpus

• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, methodology): “We follow the experimental setting in Section 3.1 and replace the base LLM with LLaMA of different sizes (7B, 13B, 35B, and 65B) to investigate the effect of model scaling on the zero-shot recommendation task.”
• Hard Negative Sampling via Large Language Models for Recommendation (2025, unknown): “The second method enhances traditional recommender systems by utilizing semantic representations generated by LLMs (Sun et al., 2024; Zheng et al., 2024; Zhu et al., 2024).”