DivRec: A Framework for Top-N Recommendation with Diversification in E-commerce

  • Kejun He
  • Junyu Niu
  • Chaofeng Sha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)


In order to increase sales for e-commerce websites and meet customer expectations, recommender systems need to recommend more niche products consumers might like. However, traditional product recommender systems usually aim to improve the recommendation accuracy while overlook the diversity within the recommendation lists. In this paper, firstly we examine the importance of diversity within recommended lists through a psychological survey. Motivated by our observations, we develop a general framework, called DivRec, to improve recommendation diversity without lowering accuracy. Experimental results on an e-commerce dataset demonstrate that our approach outperforms state-of-the-art techniques in terms of both accuracy and diversity.


Recommender Systems Collaborative Filtering Diversification 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kejun He
    • 1
  • Junyu Niu
    • 1
  • Chaofeng Sha
    • 1
  1. 1.Software SchoolFudan UniversityShanghaiP.R. China

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