Publication details
Recommendation Recovery with Adaptive Filter for Recommender Systems
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST |
| MU Faculty or unit | |
| 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. |