Personalized recommendation with corrected similarity

TL;DR

This paper introduces a corrected similarity measure for personalized recommendation systems that improves accuracy by addressing bias in similarity estimation, validated through extensive experiments on benchmark datasets.

Contribution

It proposes a novel mutual correction method for similarity estimation, enhancing recommendation accuracy over existing indices.

Findings

CSI outperforms mainstream baselines in experiments

Mutual correction reduces similarity estimation bias

Detailed analysis explains the improvement mechanism

Abstract

Personalized recommendation attracts a surge of interdisciplinary researches. Especially, similarity based methods in applications of real recommendation systems achieve great success. However, the computations of similarities are overestimated or underestimated outstandingly due to the defective strategy of unidirectional similarity estimation. In this paper, we solve this drawback by leveraging mutual correction of forward and backward similarity estimations, and propose a new personalized recommendation index, i.e., corrected similarity based inference (CSI). Through extensive experiments on four benchmark datasets, the results show a greater improvement of CSI in comparison with these mainstream baselines. And the detailed analysis is presented to unveil and understand the origin of such difference between CSI and mainstream indices.