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)


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.


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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