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Item-Based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation

  • Parivash Pirasteh
  • Jason J. Jung
  • Dosam Hwang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8398)

Abstract

User-based collaborative filtering (CF) is a widely used technique to generate recommendations. Lacking sufficient ratings will prevent CF from modeling user preference effectively and finding trustworthy similar users. To alleviate this problems, item-based CF was introduced. However, when number of co-rated items is not enough or new item is added to the system, item-based CF result is not reliable, too. This paper presents a new method based on movies similarity that focuses on improving recommendation performance when dataset is sparse. In this way, we express a new method to measure the similarity between items by utilizing the genre and director of movies. Experiments show the superiority of the measure in cold start condition.

Keywords

Recommender systems Item-based collaborative filtering Attribute correlation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Parivash Pirasteh
    • 1
  • Jason J. Jung
    • 1
  • Dosam Hwang
    • 1
  1. 1.Department of Computer EngineeringYeungnam UniversityKorea

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