BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER. An Adaptive Filter for Preference Fine-Tuning in Recommender Systems. Online. In 978-3-031-24197-0. WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021. CHAM: SPRINGER INTERNATIONAL PUBLISHING AG, 2023, p. 107-121. ISBN 978-3-031-24196-3. Available from: https://dx.doi.org/10.1007/978-3-031-24197-0_7.
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Basic information
Original name An Adaptive Filter for Preference Fine-Tuning in Recommender Systems
Authors BLANCO SÁNCHEZ, José Miguel (724 Spain, belonging to the institution), Mouzhi GE (156 China) and Tomáš PITNER (203 Czech Republic, belonging to the institution).
Edition CHAM, WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021, p. 107-121, 15 pp. 2023.
Publisher SPRINGER INTERNATIONAL PUBLISHING AG
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/23:00133947
Organization unit Faculty of Informatics
ISBN 978-3-031-24196-3
ISSN 1865-1348
Doi http://dx.doi.org/10.1007/978-3-031-24197-0_7
UT WoS 000972038800007
Keywords in English Recommender systems; Recommendation recovery; Adaptive filter; User-oriented recommendation
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 8/4/2024 12:17.
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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