Graph Learning Approaches to Recommender Systems: A Review

TL;DR

This paper systematically reviews Graph Learning based Recommender Systems (GLRS), highlighting their modeling of user preferences and item features through graph structures to enhance recommendation accuracy, reliability, and explainability.

Contribution

It provides a comprehensive characterization, formalization, and categorization of GLRS, along with a survey of recent developments and future research directions.

Findings

GLRS effectively model complex user-item relationships.

Graph learning improves recommendation accuracy and explainability.

Recent advances enable handling heterogeneous graph data.

Abstract

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics and popularity for Recommender Systems (RS). Differently from conventional RS, including content based filtering and collaborative filtering, GLRS are built on simple or complex graphs where various objects, e.g., users, items, and attributes, are explicitly or implicitly connected. With the rapid development of graph learning, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building advanced RS. In this paper, we provide a systematic review of GLRS, on how they obtain the knowledge from graphs to improve the accuracy, reliability and explainability for recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges in this new research area. Then, we survey the most recent and important developments in the area. Finally, we share some new research directions in this vibrant area.