Graph Learning based Recommender Systems: A Review
Abstract
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS 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 are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations 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. Finally, we share some new research directions in this vibrant area.
Cite
@article{arxiv.2105.06339,
title = {Graph Learning based Recommender Systems: A Review},
author = {Shoujin Wang and Liang Hu and Yan Wang and Xiangnan He and Quan Z. Sheng and Mehmet A. Orgun and Longbing Cao and Francesco Ricci and Philip S. Yu},
journal= {arXiv preprint arXiv:2105.06339},
year = {2021}
}
Comments
Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718