用图片代替长文本描述，提升大模型推荐系统效率与鲁棒性
Token-Efficient Item Representation via Images for LLM Recommender Systems
- 以图像替代冗长文本描述，降低推荐系统token消耗
- 在多个数据集上显著提升推荐效果，同时减少约40%的输入长度
- 对描述文本中的噪声不敏感，适合真实场景中不完整信息
大型语言模型（LLMs）最近成为推荐系统的重要基础。现有基于LLM的推荐系统采用两种物品表示方法：基于属性的表示和基于描述的表示。本文针对这两种方法在表示用户消费物品时存在的效率与效果权衡问题，观察到物品的图像与描述之间存在显著信息重叠，提出一种新方法——I-LLMRec。核心思路是利用图像替代冗长的文本描述来表示物品，以减少令牌使用量，同时保留描述中的丰富语义信息。大量实验表明，I-LLMRec在效率和效果上均优于现有方法。此外，该方法还能降低对描述中噪声的敏感性，提升推荐的鲁棒性。
原文摘要 · Abstract (English)
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, Item representation 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.
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