A new framework for collaborative filtering with p-moment-based similarity measure: Algorithm, optimization and application

In this paper, a general framework of user-based collaborative filtering (CF) is developed with a new p-moment-based similarity measure. The p-moment-based statistics (PMS) of individual rating data are employed to analyze the user rating habit, thereby facilitating the performance improvement of the CF algorithm. On the basis of the PMS, a new yet comprehensive similarity measure is proposed to quantify the distance between two users with focus on both the users' preferences on items and their rating habits. Compared with the traditional ones, our proposed similarity measure is more general with clearer application insights in complicated situations. Furthermore, the weights of different statistics are regarded as adjustable parameters that are determined by utilizing the particle swarm optimization technique so as to achieve good prediction performance. Based on the proposed similarity measure with optimized weights, the neighborhood set consisting of similar users is formed and then the user-based rating prediction is eventually provided. The developed CF algorithm has advantages of high prediction accuracy and wide application potential. Moreover, this CF algorithm is applied on a real-world disease (Friedreich's ataxia) assessment system in order to assist diagnosis for patients with uncertain/missing information. Experimental results demonstrate the validity and efficiency of the proposed algorithm. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper develops a general framework of user-based collaborative filtering with a new p-moment-based similarity measure, which analyzes individual rating data to improve the performance of the CF algorithm. The proposed similarity measure with optimized weights provides more accurate user-based rating prediction.