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On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi-objective Optimization-Based Composite Recommendation Framework

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Machine Intelligence Techniques for Data Analysis and Signal Processing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 997))

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Abstract

The commercial applicability of the recommendation system (RS) motivates researchers to broaden their focus from beyond accurate recommendations to diverse, novel, and serendipitous recommendations. To improve the capabilities of the personalized recommendation list of the user, we need to view the RS as the multi-objective optimization-based recommendation system. The superiority of the multi-objective recommendation model be subject to address three significant issues: (a) traditional rating evaluation approach has limited competency towards data sparsity issue which in turn affects fitness value, (b) strength of elementary objective functions lack in judging the comprehensive strength of associated component metrics, and (c) relevant recommendations failed to surprise the user with an unexpected yet novel recommendation. Hence, this research proposed a multi-objective recommendation framework (MORF) that jointly optimizes diversity and serendipity metrics with recommendation accuracy. Furthermore, the MORF integrates the reinforced similarity-based implicit similarity computation and rating prediction model to overcome the data sparsity issue in traditional rating metrics. Within MORF, we design three conflicting objective functions to develop the recommendation system’s capability to produce a diverse, surprising, yet relevant recommendation. The generated Pareto front over two benchmark data sets describes the trade-off recommendation solution. Finally, the proposed MORF is evaluated and compared with other baselines in terms of mean precision, diversity, and novelty.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, GE Road Raipur, Chhattisgarh, 492010, India

    Rahul Shrivastava, Dilip Singh Sisodia & Naresh Kumar Nagwani

Authors
  1. Rahul Shrivastava
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  2. Dilip Singh Sisodia
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  3. Naresh Kumar Nagwani
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Corresponding author

Correspondence to Rahul Shrivastava .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Dilip Singh Sisodia

  2. Department of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  3. Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

    Ram Bilas Pachori

  4. Department of Mathematics, Indian Institute of Technology Indore, Indore, India

    M. Tanveer

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Shrivastava, R., Sisodia, D.S., Nagwani, N.K. (2023). On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi-objective Optimization-Based Composite Recommendation Framework. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_1

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