Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?
This addresses a bottleneck in LLM-based recommendation systems for users and platforms needing better personalization, though it appears incremental as it builds on existing embedding and projection techniques.
The paper tackles the problem of LLMs struggling to incorporate collaborative filtering signals in recommendation tasks by proposing a method that projects user and item embeddings into the LLM token space. Preliminary results show this improves recommendation performance over text-only LLM baselines.
Large Language Models (LLMs) have emerged as promising recommendation systems, offering novel ways to model user preferences through generative approaches. However, many existing methods often rely solely on text semantics or incorporate collaborative signals in a limited manner, typically using only user or item embeddings. These methods struggle to handle multiple item embeddings representing user history, reverting to textual semantics and neglecting richer collaborative information. In this work, we propose a simple yet effective solution that projects user and item embeddings, learned from collaborative filtering, into the LLM token space via separate lightweight projector modules. A finetuned LLM then conditions on these projected embeddings alongside textual tokens to generate recommendations. Preliminary results show that this design effectively leverages structured user-item interaction data, improves recommendation performance over text-only LLM baselines, and offers a practical path for bridging traditional recommendation systems with modern LLMs.