Personalized movie recommendation system based on support vector machine and improved particle swarm optimization

Xibin Wang, Fengji Luo, Chunyan Sang, Jun Zeng, Sachio Hirokawa

    研究成果: Contribution to journalArticle査読

    9 被引用数 (Scopus)

    抄録

    With the rapid development of information andWeb technologies, people are facing 'information overload' in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

    本文言語英語
    ページ(範囲)285-293
    ページ数9
    ジャーナルIEICE Transactions on Information and Systems
    E100D
    2
    DOI
    出版ステータス出版済み - 2 2017

    All Science Journal Classification (ASJC) codes

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence

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