Content-Based Top-N Recommendation using Heterogeneous Relations

TL;DR

This paper introduces a content-based Top-N recommender system that leverages heterogeneous relations and global term weights to effectively address data sparsity and cold-start issues, outperforming existing methods.

Contribution

The paper proposes a novel method that learns global term weights using PathSim for heterogeneous profiles, enhancing recommendation accuracy in sparse data scenarios.

Findings

The proposed method outperforms baseline recommenders in experiments.

Global term weights improve profile and activity information integration.

The matrix reformulation enables efficient parallel training.

Abstract

Top-$N$ recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-based top-$N$ recommender system by learning the global term weights in profiles. To achieve this, we bring in PathSim, which could well measures the node similarity with heterogeneous relations (between users and items). Starting from the original TF-IDF value, the global term weights gradually converge, and eventually reflect both profile and activity information. To facilitate training, the derivative is reformulated into matrix form, which could easily be paralleled. We conduct extensive experiments, which demonstrate the superiority of the proposed method.