基於大語言模型的推薦系統用戶畫像管理

LLM-based User Profile Management for Recommender System

February 20, 2025

作者: Seunghwan Bang, Hwanjun Song

cs.AI

摘要

大型語言模型（LLMs）的快速發展為推薦系統開闢了新的可能性，使其能夠在不進行傳統訓練的情況下實現零樣本推薦。儘管這些模型具有巨大潛力，但現有研究大多僅依賴用戶的購買歷史，這使得通過整合用戶生成的文本數據（如評論和產品描述）來提升推薦效果仍有顯著空間。針對這一不足，我們提出了PURE，這是一種基於LLM的新型推薦框架，它通過系統性地提取和總結用戶評論中的關鍵信息來構建並維護不斷演化的用戶畫像。PURE包含三個核心組件：用於識別用戶偏好和關鍵產品特徵的評論提取器、用於精煉和更新用戶畫像的畫像更新器，以及利用最新畫像生成個性化推薦的推薦器。為了評估PURE，我們引入了一種連續序列推薦任務，該任務通過隨時間添加評論並逐步更新預測來反映真實世界場景。我們在Amazon數據集上的實驗結果表明，PURE在有效利用長期用戶信息的同時，成功應對了token限制，其表現優於現有的基於LLM的方法。

English

The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.