Diffusion-like recommendation with enhanced similarity of objects

TL;DR

This paper introduces a tunable exponent to enhance resource allocation in diffusion-like recommendation models, improving both diversity and accuracy by better promoting unpopular objects, validated on three benchmark datasets.

Contribution

It proposes a novel method to improve recommendation performance by adjusting resource transfer similarity with a tunable exponent, balancing diversity and accuracy.

Findings

Significant performance improvements on MovieLens, Netflix, and RateYourMusic datasets.

Enhanced recommendation scores for unpopular objects.

Better balance between diversity and accuracy.

Abstract

In last decades, diversity and accuracy have been regarded as two important measures in evaluating a recommendation model. However, a clear concern is that a model focusing excessively on one measure will put the other one at risk, thus it is not easy to greatly improve diversity and accuracy simultaneously. In this paper, we propose to enhance the Resource-Allocation (RA) similarity in resource transfer equations of diffusion-like models, by giving a tunable exponent to the RA similarity, and traversing the value of the exponent to achieve the optimal recommendation results. In this way, we can increase the recommendation scores (allocated resource) of many unpopular objects. Experiments on three benchmark data sets, MovieLens, Netflix, and RateYourMusic show that the modified models can yield remarkable performance improvement compared with the original ones.