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Improving the Performance of Collaborative Filtering with Category-Specific Neighborhood

  • Karnam Dileep KumarEmail author
  • Polepalli Krishna Reddy
  • Pailla Balakrishna Reddy
  • Longbing Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9622)

Abstract

Recommender system (RS) helps customers to select appropriate products from millions of products and has become a key component in e-commerce systems. Collaborative filtering (CF) based approaches are widely employed to build RSs. In CF, recommendation to the target user is computed after forming the corresponding neighbourhood of users. Neighborhood of a target user is extracted based on the similarity between the product rating vector of the target user and the product rating vectors of individual users. In CF, the methodology employed for neighborhood formation influences the performance. In this paper, we have made an effort to improve the performance of CF by proposing a different approach to compute recommendations by considering two kinds of neighborhood. One is the neighborhood by considering the product ratings of the user as a single vector and the other is based on the neighborhood of the corresponding virtual users. For the target user, the virtual users are formed by dividing the ratings based on the category of products. We have proposed a combined approach to compute better recommendations by considering both kinds of neighborhoods. The experiments results on real world MovieLens dataset show that the proposed approach improves the performance over CF.

Keywords

Data mining Recommender systems Collaborative filtering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Karnam Dileep Kumar
    • 1
    Email author
  • Polepalli Krishna Reddy
    • 1
  • Pailla Balakrishna Reddy
    • 1
  • Longbing Cao
    • 2
  1. 1.International Institute of Information Technology HyderabadHyderabadIndia
  2. 2.Advanced Analytics InstituteUniversity of TechnologySydneyAustralia

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