Degree correlation effect of bipartite network on personalized recommendation

TL;DR

This paper introduces a modified collaborative filtering algorithm that incorporates degree correlation between users and objects, significantly improving accuracy, diversity, and reducing popularity bias in personalized recommendations.

Contribution

The paper proposes a novel similarity index based on diffusion processes that embeds degree correlation, enhancing recommendation accuracy and diversity over standard methods.

Findings

Recommendation accuracy improved by 18.19%

Diversity increased by 21.90%

Algorithm favors less popular items

Abstract

In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.