Abstract

Recommender systems aim to predict user preferences by analyzing users' past behavior. Collaborative filtering (CF) is one of the key techniques employed in recommender systems that uses explicit ( e.g. , ratings) and implicit ( e.g. , browsing) feedback from users to predict unknown feedback, providing top- N recommendations. However, CF faces challenges when dealing with sparse data, which can decrease the accuracy of recommendations. To overcome these inherent challenges in recommender systems, this article introduces the concept of "uninteresting items" that have not been rated by a user, but are unlikely to be liked even when recommended. We then review our previous works that utilize both positive preferences from rated items and negative preferences from uninteresting items to improve recommendation accuracy. Specifically, we discuss a family of our eight CF methods that are assisted by the uninteresting items: Zero-injection (ZI), l -injection, Imputation, RAGAN, and Deep-ZI, which are designed for explicit feedback, as well as gOCCF, M-BPR, and CNS, which are designed for implicit feedback. Also, we report some evaluation results for showing their effectiveness.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.