基于图学习的推荐系统研究综述

Survey of Recommender Systems Based on Graph Learning

协同过滤是一种被广泛应用于推荐系统中的方法,其利用不同用户之间(或不同物品之间)的相似性关系来过滤和抽取用户和物品的交互信息,从而进行用户推荐。近年来,图神经网络因其出色的表示学习性能和良好的可扩展性逐渐成为推荐领域中的一种新兴的范式。文中从图学习角度对近年来推荐领域的研究进行系统性的回顾与总结。首先,根据数据类型将推荐场景分成两类,包括基于交互信息的推荐系统(将用户与物品交互数据作为关键数据源)和辅助信息增强的推荐系统(融入与用户和物品相关联的社交信息和知识图谱信息);其次,从随机游走、图表示学习和图神经网络方面入手,对不同推荐场景中的方法、关键技术、主要难点和重要进展进行回顾与总结;最后,总结关于图学习方法在推荐领域中面临的挑战和未来的主要研究方向。

Collaborative filtering is a widely used technique in current recommendation systems.It leverages the similarity between different users or items to retrieve interactive information between users and items and recommends new items for target users.In recent years, graph learning has gradually become an emerging recommendation paradigm due to its excellent perfor-mance and scalability in graph representation learning.This paper systematically reviews the most recent research on recommendation field from the perspective of graph learning.First, we provide a taxonomy that groups the current recommendation scenarios into two categories according to the data type used, including recommendation systems based on interactive information that leverage user-item interaction data as the main data source and auxiliary information-enhanced recommendation systems that incorporate social information associated with users and items as well as the knowledge graph information.Then, we review the main approaches, fundamental algorithms and critical difficulties of current recommendation models from the perspectives of random walk, graph representation learning and graph neural networks.Finally, we summarize the main challenges of graph learning methods in the field of recommendation system and outline the possible future research directions.