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International Conference on Hybrid Intelligent Systems

HIS 2019: Hybrid Intelligent Systems pp 113–122Cite as

Diversity in Recommendation System: A Cluster Based Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1179))

Abstract

The recommendation system is used to process a large amount of data to recommend new item to users, which are achieved using the many developed algorithms. Hence, it is a challenging task for lots of online applications to establish an efficient algorithm for a recommendation system that follows a good trade-off between accuracy and diversity. Diversity in recommendation systems is used to avoid the overfitting problem as well as excellent skill, which provides a recommendation based on increasing the quality of user experiences. In this paper, we proposed a methodology of recommendation to the user with diversity. The impact of diversity on the system leads to user experience for new items. The aim of this paper is to provide a brief overview of diversification with state of the art. A further similarity measure based on heuristic similarity measure “proximity impact popularity” is used to provide a new model with the better-personalized recommendation. The proposed approach gives profitability to many applications for better user experience and diverse item recommendations.

Supported by Indian Institute of Technology (BHU), Varanasi.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology (BHU), Varanasi, India

    Naina Yadav, Rajesh Kumar Mundotiya, Anil Kumar Singh & Sukomal Pal

Authors
  1. Naina Yadav
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  2. Rajesh Kumar Mundotiya
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  3. Anil Kumar Singh
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  4. Sukomal Pal
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Corresponding author

Correspondence to Naina Yadav .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. School of Computer Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India

    Shishir K. Shandilya

  3. Area of Project Engineering, University of Cordoba, Córdoba, Spain

    Laura Garcia-Hernandez

  4. Escola de Engenharia, Universidade do Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Cite this paper

Yadav, N., Mundotiya, R.K., Singh, A.K., Pal, S. (2021). Diversity in Recommendation System: A Cluster Based Approach. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_12

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