A Hybrid Framework for Improving Diversity and Long Tail Items in Recommendations

  • Pragati Agarwal
  • Rama Syamala Sreepada
  • Bidyut Kr. PatraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)


In today’s information overloaded era, recommender system is a necessity and it is widely used in most of the domains of e-commerce. Over the years, recommender system is improved to meet the main purpose of achieving better user experience, where accuracy is considered as one of the important aspects in its design. However, other aspects such as diversity, long tail item recommendation, novelty and serendipity are equally important while providing recommendations to the users. Research to improve above mentioned aspects is limited. In this paper, we propose an efficient approach to improve diversity and long tail item recommendations. The experiments are conducted on two real world movie rating datasets namely, MovieLens and Netflix. Experimental analysis shows that the proposed method outperforms the state-of-the art approaches in recommending diverse and long tail items.


Recommender system Diversity Collaborative Filtering Long tail items Hybrid Reranking Framework 



This work is partially funded by SERB, Department of Science and Technology, Govt. of India, Grant No. EMR/2017/004357, dated: 18th June 2018.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pragati Agarwal
    • 1
  • Rama Syamala Sreepada
    • 1
  • Bidyut Kr. Patra
    • 1
    Email author
  1. 1.National Institute of Technology RourkelaRourkelaIndia

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