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Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

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Abstract

One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets.

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Notes

  1. http://www.last.fm

  2. For the SR hyper-parameter, we used values in [1,15] and for SKNN values in [20,150].

  3. The hyper-parameters of these baselines are tuned using a validation set.

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Funding

This work was executed within the imec.icon project NewsButler. The NewsButler project is co-financed by imec and received project support from Flanders Innovation and Entrepreneurship (project no. HBC.2017.0628).

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Authors and Affiliations

  1. Itec, imec research group at KU Leuven, Kortrijk, Belgium

    Alireza Gharahighehi & Celine Vens

  2. Department of Public Health and Primary Care, KU Leuven, Campus KULAK, Kortrijk, Belgium

    Alireza Gharahighehi & Celine Vens

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  1. Alireza Gharahighehi
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Correspondence to Alireza Gharahighehi.

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This article is part of the topical collection “Advanced Theories and Algorithms for Next-generation Recommender Systems” guest edited by Shoujin Wang, Lin Xiao, Marko Tkalcic and Julian McAuley.

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Gharahighehi, A., Vens, C. Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems. SN COMPUT. SCI. 2, 39 (2021). https://doi.org/10.1007/s42979-020-00399-2

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