Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization
The implement of paper “Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization”
Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art
recommendation performance. In the ubiquitous implicit feedback based CF, users’ unobserved behaviors are treated as unlinked edges in the user-item bipartite graph.
As users’ unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links.
In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other
in CF, as updated node embeddings are learned from previous graph structure, and vice versa (i.e., newly updated graph structure are