Top-N Recommendation on Graphs

TL;DR

This paper introduces a graph-based recommendation algorithm that leverages structural and similarity information in user-item matrices, using manifold learning and convex optimization to improve recommendation accuracy.

Contribution

It proposes a novel graph-based representation method that exploits affinity and structure information, solved efficiently via a Sylvester equation, enhancing collaborative filtering.

Findings

Outperforms existing methods on six benchmark datasets

Effectively captures user and item proximities

Improves recommendation accuracy in sparse matrices

Abstract

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well-known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.