基于图学习的推荐系统研究综述

doi:10.11896/jsjkx.210900072

Abstract

Abstract: 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.

Key words: Recommender system, Collaborative filtering, Graph learning

CLC Number:

TP181

CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning[J].Computer Science, 2022, 49(9): 1-13.

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