Publication details

Recommendation Recovery with Adaptive Filter for Recommender Systems

Authors

BLANCO SÁNCHEZ José Miguel GE Mouzhi PITNER Tomáš

Year of publication 2021
Type Article in Proceedings
Conference Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
Citation
Doi http://dx.doi.org/10.5220/0010653600003058
Keywords Recommender Systems, Recommendation Recovery, Adaptive Filter, User-oriented Recommendation.
Description 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.

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