面向推荐系统的用户兴趣建模综述

doi:10.3778/j.issn.1002-8331.2501-0190

摘要/Abstract

摘要： 聚焦用户兴趣建模任务，对点击意图识别和兴趣构建方法进行归纳分析，并探讨该领域现存挑战。用户兴趣建模包括点击意图识别和兴趣构建两个级联阶段。根据是否对用户点击行为涉及特征给予差异化关注，将点击意图识别方法归纳为个性化和非个性化两类；根据对用户点击意图序列处理方式的不同，将兴趣构建方法划分为聚集式和生成式两类，为该领域提供清晰的研究思路。在ml-20m和Amazon_all_beauty数据集上进行实验，采用Recall、Precision、MRR和NDCG作为评价指标，验证各类兴趣构建方法的优势与不足。用户兴趣建模能够依据行为序列及其上下文信息构建兴趣表示，帮助模型学习用户行为之间的隐含关系进而实现个性化推荐服务。但是该任务仍面临一些挑战，例如个性化点击意图识别方法未充分探索一元点击意图之间的潜在关联性，兴趣构建阶段需要深刻认知兴趣多样性从而捕捉不同粒度的用户兴趣等。

关键词: 推荐系统, 兴趣建模, 用户点击意图

Abstract: Focusing on the task of user interest modeling, this paper summarizes and analyzes the methods of click intention identification and interest construction, and discusses the existing challenges in this field. User interest modeling includes two cascade stages: click intention recognition and interest construction. According to whether or not differential attention is paid to the features involved in user click behavior, click intention recognition methods are classified into two categories: personalized and non-personalized. According to the different processing methods of user click intention sequence, the interest construction method is divided into two categories: aggregation and generation, which provides clear research ideas for this field. Experiments are carried out on ml-20m and Amazon_all_beauty datasets, and Recall, Precision, MRR and NDCG are used as evaluation indicators to verify the advantages and disadvantages of various interest construction methods. User interest modeling can construct interest representation according to behavior sequence and its context information, help the model learn the implicit relationship between user behaviors, and then realize personalized recommendation service. However, this task still faces some challenges, such as personalized click intention recognition method does not fully explore the potential correlation between unary click intentions, interest construction phase needs a deep understanding of interest diversity in order to capture different granularity of user interests and so on.

Key words: recommendation systems, interest modeling, user click intention

吕学强, 王夏雨, 马登豪. 面向推荐系统的用户兴趣建模综述[J]. 计算机工程与应用, 2025, 61(21): 15-29.

LYU Xueqiang, WANG Xiayu, MA Denghao. Survey of User Interest Modeling for Recommendation Systems[J]. Computer Engineering and Applications, 2025, 61(21): 15-29.

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