Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models
This work addresses the problem of enhancing recommendation accuracy for users by combining existing methods, representing an incremental advancement in the field.
This paper tackles sequential recommendation by integrating traditional recommendation systems with large language models (LLMs) through a new concept called RSLLM, which uses a prompting method and two-stage fine-tuning to align ID embeddings with LLMs, achieving improved performance as validated by empirical results.
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.