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Incremental SVD-Based Collaborative Filtering Enhanced with Diversity for Personalized Recommendation

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

Along with the rapid rise of the internet, an e-commerce website brings enormous benefits for both customers and vendors. However, many choices are given at the same time makes customers have difficulty in choosing the most suitable products. A rising star solution for this is the recommender system which helps to narrow down the amount of suitable and relevant products for each customer. Matrix factorization is one of the most popular techniques used in recommender systems because of its effectiveness and simplicity. In this paper, we introduce a matrix factorization-based recommender system using Singular Value Decomposition (SVD) with some improvements in collaborative filtering and incremental learning. The SVD-based collaborative filtering methods can help generate personalized recommendations by combining user profiles. Moreover, the recommendation lists generated by the system are enhanced with diversity, which might attract more customer interests. Amazon’s Electronic data set is used to evaluate our proposed framework of the SVD-based recommender system. The experimental results show that our framework is promising.

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Acknowledgment

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number: 06/2018/TN.

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Authors and Affiliations

  1. School of Computer Science and Engineering, International University, Vietnam National University, HCMC, Ho Chi Minh City, Vietnam

    Minh Quang Pham, Thi Thanh Sang Nguyen & Pham Minh Thu Do

  2. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Adrianna Kozierkiewicz

Authors
  1. Minh Quang Pham
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  2. Thi Thanh Sang Nguyen
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  3. Pham Minh Thu Do
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  4. Adrianna Kozierkiewicz
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Corresponding author

Correspondence to Thi Thanh Sang Nguyen .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Pham, M.Q., Nguyen, T.T.S., Do, P.M.T., Kozierkiewicz, A. (2020). Incremental SVD-Based Collaborative Filtering Enhanced with Diversity for Personalized Recommendation. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

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