Abstract
Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs’ interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users’ earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods, especially achieving a 124.3% to 293.7% improvement over SOTA LLM-based methods in direct recommendations. Our code is available online.- Anthology ID:
- 2024.emnlp-main.653
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11696–11711
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.653
- DOI:
- 10.18653/v1/2024.emnlp-main.653
- Cite (ACL):
- Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, and Yoshimi Suzuki. 2024. Enhancing High-order Interaction Awareness in LLM-based Recommender Model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11696–11711, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing High-order Interaction Awareness in LLM-based Recommender Model (Wang et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.653.pdf