[2503.06238] Token-Efficient Item Representation via Images for LLM Recommender Systems

Computer Science > Information Retrieval arXiv:2503.06238 (cs) [Submitted on 8 Mar 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Token-Efficient Item Representation via Images for LLM Recommender Systems Authors:Kibum Kim, Sein Kim, Hongseok Kang, Jiwan Kim, Heewoong Noh, Yeonjun In, Kanghoon Yoon, Jinoh Oh, Julian McAuley, Chanyoung Park View a PDF of the paper titled Token-Efficient Item Representation via Images for LLM Recommender Systems, by Kibum Kim and 9 other authors View PDF HTML (experimental) Abstract: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 outperform...