User collaborative filtering recommendation algorithm based on adaptive parametric optimisation SSPSO. (2017)

• Record Type: Journal Article
• Authors: Pan, Xiuqin; Zhou, Wenmin; Lu, Yong; Li, Ruixiang
• Is Part Of: International journal of computing science and mathematics. Volume 8:Number 6(2017)
• Page Start: 580
• Page End: 592
• Publication Date: 2017
• Subjects: user collaborative filtering -- recommendation algorithm -- particle swarm optimisation -- PSO -- semi-supervised learning; Mathematics -- Periodicals; Computer science -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.285
• Languages: English
• ISSNs: 1752-5055

Abstract

Recommendation algorithm is one of the hot issues in the field of computer science, and is widely used in many aspects. Various types of e-commerce systems and applications need to use recommendation system to support. Collaborative filtering recommendation algorithm has been widely used in e-commerce system for its high recommendation accuracy. In order to improve the performance of the process of clustering and selection of nearest neighbours in collaborative filtering, there are several optimisation proposals in this paper directly to the recommendation algorithm which is based on the adaptive parametric optimisation semi-supervised PSO clustering (APO_SSPSO). This paper uses MovieLens data sets to compare the performance of the proposed method and the traditional collaborative filtering recommendation algorithm. Simulation has proved that this recommendation algorithm has accuracy and effectiveness in enhancing the performance of user collaborative filtering recommendation system. And to some extent, the algorithm has minimised space consumption.