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

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

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)285-293
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE100D
Issue number2
DOIs
Publication statusPublished - Feb 2017

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Recommender systems
Particle swarm optimization (PSO)
Support vector machines
Collaborative filtering
Information technology

All Science Journal Classification (ASJC) codes

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

Cite this

Personalized movie recommendation system based on support vector machine and improved particle swarm optimization. / Wang, Xibin; Luo, Fengji; Sang, Chunyan; Zeng, Jun; Hirokawa, Sachio.

In: IEICE Transactions on Information and Systems, Vol. E100D, No. 2, 02.2017, p. 285-293.

Research output: Contribution to journalArticle

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