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An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

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Web Information Systems and Technologies (WEBIST 2020, WEBIST 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 469))

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Abstract

A recommender system may recommend certain items that the users would not prefer. This can be caused by either the imperfection of the recommender system or the change of user preferences. When those failed recommendations appear often in the system, the users may consider that the recommender system is not able to capture the user preference. This can result in abandoning to further use the recommender system. However, given the possible failed recommendations, most recommender systems will ignore the non-preferred recommendations. Therefore, this paper proposes failure recovery solution for recommender systems with an adaptive filter. On the one hand, the proposed solution can deal with the failed recommendations while keeping the user engagement. Additionally, it allows the recommender system to dynamically fine tune the preferred items and become a long-term application. Also, the adaptive filter can avoid the cost of constantly updating the recommender learning model.

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Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    José Miguel Blanco & Tomáš Pitner

  2. Deggendorf Institute of Technology, Deggendorf, Germany

    Mouzhi Ge

Authors
  1. José Miguel Blanco
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  2. Mouzhi Ge
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  3. Tomáš Pitner
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Correspondence to José Miguel Blanco .

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Editors and Affiliations

  1. University of Padua, Padua, Italy

    Massimo Marchiori

  2. University of Seville, Seville, Spain

    Francisco José Domínguez Mayo

  3. Polytechnic Institute of Setúbal/INSTICC, Setubal, Portugal

    Joaquim Filipe

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Blanco, J.M., Ge, M., Pitner, T. (2023). An Adaptive Filter for Preference Fine-Tuning in Recommender Systems. In: Marchiori, M., Domínguez Mayo, F.J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST WEBIST 2020 2021. Lecture Notes in Business Information Processing, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-24197-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-24197-0_7

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