Graph Learning based Recommender Systems: A Review

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

doi:10.24963/ijcai.2021/630

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

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4644-4652.

https://doi.org/10.24963/ijcai.2021/630

Keywords

Machine learning: General
Multidisciplinary topics and applications: General
Planning and scheduling: General
Knowledge representation and reasoning: General

Abstract

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.

Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, and Philip S. Yu. 2021. Graph Learning based Recommender Systems: A Review. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4644–4652. https://doi.org/10.24963/ijcai.2021/630

@inproceedings{ijcai2021p630,
  title     = {Graph Learning based Recommender Systems: A Review},
  author    = {Wang, Shoujin and Hu, Liang and Wang, Yan and He, Xiangnan and Sheng, Quan Z. and Orgun, Mehmet A. and Cao, Longbing and Ricci, Francesco and Yu, Philip S.},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4644--4652},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/630},
  url       = {https://doi.org/10.24963/ijcai.2021/630},
}

TY  - CPAPER
TI  - Graph Learning based Recommender Systems: A Review
AU  - Wang, Shoujin
AU  - Hu, Liang
AU  - Wang, Yan
AU  - He, Xiangnan
AU  - Sheng, Quan Z.
AU  - Orgun, Mehmet A.
AU  - Cao, Longbing
AU  - Ricci, Francesco
AU  - Yu, Philip S.
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4644
EP  - 4652
DO  - 10.24963/ijcai.2021/630
UR  - https://doi.org/10.24963/ijcai.2021/630
ER  -