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World Wide Web

, Volume 20, Issue 4, pp 815–829 | Cite as

An empirical study on user-topic rating based collaborative filtering methods

  • Tieke He
  • Zhenyu ChenEmail author
  • Jia Liu
  • Xiaofang Zhou
  • Xingzhong Du
  • Weiqing Wang
Article

Abstract

User based collaborative filtering (CF) has been successfully applied into recommender system for years. The main idea of user based CF is to discover communities of users sharing similar interests, thus, in which, the measurement of user similarity is the foundation of CF. However, existing user based CF methods suffer from data sparsity, which means the user-item matrix is often too sparse to get ideal outcome in recommender systems. One possible way to alleviate this problem is to bring new data sources into user based CF. Thanks to the rapid development of social annotation systems, we turn to using tags as new sources. In these approaches, user-topic rating based CF is proposed to extract topics from tags using different topic model methods, based on which we compute the similarities between users by measuring their preferences on topics. In this paper, we conduct comparisons between three user-topic rating based CF methods, using PLSA, Hierarchical Clustering and LDA. All these three methods calculate user-topic preferences according to their ratings of items and topic weights. We conduct the experiments using the MovieLens dataset. The experimental results show that LDA based user-topic rating CF and Hierarchical Clustering outperforms the traditional user based CF in recommending accuracy, while the PLSA based user-topic rating CF performs worse than the traditional user based CF.

Keywords

Recommender systems Collaborative filtering PLSA Hierarchical clustering LDA 

Notes

Acknowledgments

This work is supported in part by the National Key Research and Development Program of China (2016YFC0800805), National Basic Research Program of China (973 Program 2014CB340702), the National Natural Science Foundation of China (Grant No. 61170067).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Tieke He
    • 1
  • Zhenyu Chen
    • 1
    Email author
  • Jia Liu
    • 1
  • Xiaofang Zhou
    • 2
  • Xingzhong Du
    • 2
  • Weiqing Wang
    • 2
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Information Technology & Electrical EngineeringThe University of QueenslandSt LuciaAustralia

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