Graph learning based recommender systems: a review

Shoujin Wang, Liang Hu, Yan Wang*, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.
Original languageEnglish
Title of host publicationProceedings of the 30th International Joint Conference on Artificial Intelligence
EditorsZhi-Hua Zhou
Place of PublicationFreiburg, Germany
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages9
ISBN (Electronic)9780999241196
Publication statusPublished - 2021
Event30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Montreal, Canada
Duration: 19 Aug 202127 Aug 2021


Conference30th International Joint Conference on Artificial Intelligence, IJCAI 2021


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