Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models

• URL: http://arxiv.org/abs/2412.16933v1
• Date: Sun, 22 Dec 2024 09:08:46 GMT
• Title: Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models
• Authors: Kai Zheng, Qingfeng Sun, Can Xu, Peng Yu, Qingwei Guo,
• Abstract summary: We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM) RSLLM uses a unique prompting method that combines ID-based item embeddings from conventional recommendation models with textual item features. It treats users' sequential behaviors as a distinct language and aligns the ID embeddings with the LLM's input space using a projector.
• Score: 33.02146794292383
• License:
• Abstract: This paper explores the use of Large Language Models (LLMs) for sequential recommendation, which predicts users' future interactions based on their past behavior. We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs. RSLLM uses a unique prompting method that combines ID-based item embeddings from conventional recommendation models with textual item features. It treats users' sequential behaviors as a distinct language and aligns the ID embeddings with the LLM's input space using a projector. We also propose a two-stage LLM fine-tuning framework that refines a pretrained LLM using a combination of two contrastive losses and a language modeling loss. The LLM is first fine-tuned using text-only prompts, followed by target domain fine-tuning with unified prompts. This trains the model to incorporate behavioral knowledge from the traditional sequential recommender into the LLM. Our empirical results validate the effectiveness of our proposed framework.

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