An optimally weighted user- and item-based collaborative filtering approach to predicting baseline data for Friedreich's Ataxia patients

NEUROCOMPUTING

卷:419

Article

Yue, Wenbin¹  Wang, Zidong¹  Liu, Weibo¹  Tian, Bo²  Lauria, Stanislao¹  Liu, Xiaohui¹ 

[1] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England

[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

关键词: Friedreich's Ataxia;    Collaborative filtering;    Positive correlation;    Negative correlation;    Particle swarm optimization;   

DOI  :  10.1016/j.neucom.2020.08.031

来源: Elsevier

【 摘 要 】

In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is developed for recommendation systems with application in predicting baseline data of Friedreich's Ataxia (FRDA) patients. The proposed MCF algorithm combines the individual merits of both the user-based collaborative filtering (UBCF) method and the item-based collaborative filtering (IBCF) method, where both the positively and negatively correlated neighbors are taken into account. The weighting parameters are introduced to quantify the degrees of utilizations of the UBCF and IBCF methods in the rating prediction, and the particle swarm optimization algorithm is applied to optimize the weighting parameters in order to achieve an adequate tradeoff between the positively and negatively correlated neighbors in terms of predicting the rating values. To demonstrate the prediction performance of the proposed MCF algorithm, the developed MCF algorithm is employed to assist with the baseline data collection for the FRDA patients. The effectiveness of the proposed MCF algorithm is confirmed by extensive experiments and, furthermore, it is shown that our algorithm outperforms some conventional approaches. (c) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

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