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.
Description
[2105.06339] Graph Learning based Recommender Systems: A Review
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