Recommendation Recovery with Adaptive Filter for Recommender Systems

BLANCO SÁNCHEZ, José Miguel, Mouzhi GE and Tomáš PITNER. Recommendation Recovery with Adaptive Filter for Recommender Systems. Online. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST. Setúbal, Portugal: SciTePress/INSTICC, 2021, p. 283-290. ISBN 978-989-758-536-4. Available from: https://dx.doi.org/10.5220/0010653600003058.

Basic information

• Original name: Recommendation Recovery with Adaptive Filter for Recommender Systems
• Authors: BLANCO SÁNCHEZ, José Miguel (724 Spain, guarantor, belonging to the institution), Mouzhi GE (156 China) and Tomáš PITNER (203 Czech Republic, belonging to the institution).
• Edition: Setúbal, Portugal, Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST, p. 283-290, 8 pp. 2021.
• Publisher: SciTePress/INSTICC

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/21:00122770
• Organization unit: Faculty of Informatics
• ISBN: 978-989-758-536-4
• ISSN: 2184-3252
• Doi: http://dx.doi.org/10.5220/0010653600003058
• UT WoS: 000795868100028
• Keywords in English: Recommender Systems; Recommendation Recovery; Adaptive Filter; User-oriented Recommendation
• Tags: firank_B

Abstract

Most recommender systems are focused on suggesting the optimal recommendations rather than finding a way to recover from a failed recommendation. Thus, when a failed recommendation appears several times, users may abandon to use a recommender system by considering that the system does not take her preference into account. One of the reasons is that when a user does not like a recommendation, this preference cannot be instantly captured by the recommender learning model, since the learning model cannot be constantly updated. Although this can be to some extent alleviated by critique-based algorithms, fine tuning the preference is not capable of fully expelling not-preferred items. This paper is therefore to propose a recommender recovery solution with an adaptive filter to deal with the failed recommendations while keeping the user engagement and, in turn, allow the recommender system to become a long-term application. It can also avoid the cost of constantly updating the recommender learning model.