How Does Message Passing Improve Collaborative Filtering?

arXiv:2404.08660v1 Announce Type: cross
Abstract: Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF. They assume that message passing helps CF methods in a manner akin to its benefits for graph-based learning tasks in general. However, even …

abstract applications arxiv capabilities collaborative collaborative filtering cs.ir cs.lg data filtering graph graph neural networks graphs knowledge networks neural networks recommender systems research results structured data systems type world