Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens

• URL: http://arxiv.org/abs/2406.08477v1
• Date: Wed, 12 Jun 2024 17:59:05 GMT
• Title: Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
• Authors: Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen, Kai-Qi Liu, De-Chuan Zhan, Han-Jia Ye,
• Abstract summary: We show how to effectively tokenize users and items in Large Language Models (LLMs)-based recommender systems. We emphasize the role of out-of-vocabulary (OOV) tokens in addition to the in-vocabulary ones. Our proposed framework outperforms existing state-of-the-art methods across various downstream recommendation tasks.
• Score: 51.584024345378005
• License:
• Abstract: Characterizing users and items through vector representations is crucial for various tasks in recommender systems. Recent approaches attempt to apply Large Language Models (LLMs) in recommendation through a question and answer format, where real users and items (e.g., Item No.2024) are represented with in-vocabulary tokens (e.g., "item", "20", "24"). However, since LLMs are typically pretrained on natural language tasks, these in-vocabulary tokens lack the expressive power for distinctive users and items, thereby weakening the recommendation ability even after fine-tuning on recommendation tasks. In this paper, we explore how to effectively tokenize users and items in LLM-based recommender systems. We emphasize the role of out-of-vocabulary (OOV) tokens in addition to the in-vocabulary ones and claim the memorization of OOV tokens that capture correlations of users/items as well as diversity of OOV tokens. By clustering the learned representations from historical user-item interactions, we make the representations of user/item combinations share the same OOV tokens if they have similar properties. Furthermore, integrating these OOV tokens into the LLM's vocabulary allows for better distinction between users and items and enhanced capture of user-item relationships during fine-tuning on downstream tasks. Our proposed framework outperforms existing state-of-the-art methods across various downstream recommendation tasks.

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