Image is All You Need: Towards Efficient and Effective Large Language Model-Based Recommender Systems
This work addresses efficiency and robustness issues in LLM-based recommender systems for users and platforms, though it is incremental as it builds on existing representation approaches.
The paper tackles the trade-off between efficiency and effectiveness in LLM-based recommender systems by proposing I-LLMRec, which uses images instead of lengthy textual descriptions to represent items, resulting in reduced token usage and improved performance with concrete gains in metrics like accuracy and efficiency.
Large Language Models (LLMs) have recently emerged as a powerful backbone for recommender systems. Existing LLM-based recommender systems take two different approaches for representing items in natural language, i.e., Attribute-based Representation and Description-based Representation. In this work, we aim to address the trade-off between efficiency and effectiveness that these two approaches encounter, when representing items consumed by users. Based on our interesting observation that there is a significant information overlap between images and descriptions associated with items, we propose a novel method, Image is all you need for LLM-based Recommender system (I-LLMRec). Our main idea is to leverage images as an alternative to lengthy textual descriptions for representing items, aiming at reducing token usage while preserving the rich semantic information of item descriptions. Through extensive experiments, we demonstrate that I-LLMRec outperforms existing methods in both efficiency and effectiveness by leveraging images. Moreover, a further appeal of I-LLMRec is its ability to reduce sensitivity to noise in descriptions, leading to more robust recommendations.