Wasilewski, JacekJacekWasilewskiHurley, Neil J.Neil J.Hurley2019-07-022019-07-022019 Assoc2019-06-12978-1-4503-6021-0http://hdl.handle.net/10197/10834UMAP '19: 27th ACM Conference on User Modeling, Adaptation and Personalization, Larnaca, Cyprus, 9–12 June 2019Novelty enhancement of recommendations is typically achieved through a post-filtering process applied on a candidate set of items. While it is an effective method, its performance heavily depends on the quality of a baseline algorithm, and many of the state-of-the-art algorithms generate recommendations that are relatively similar to what the user has interacted with in the past. In this paper we explore the use of sampling as a means of novelty enhancement in the Bayesian Personalized Ranking objective. We evaluate the proposed extensions on the MovieLens 20M dataset, and show that the proposed method can be successfully used instead of two-step re-ranking, as it offers comparable and better accuracy/novelty tradeoffs, and more unique recommendations.en© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UMAP '19 Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (2019) http://doi.acm.org/10.1145/3320435.3320468Recommender systemsInformation retrievalRetrieval models and rankingRetrieval tasks and goalsNovelty in information retrievalConference Publication14414810.1145/3320435.33204682019-06-28SFI/12/RC/2289https://creativecommons.org/licenses/by-nc-nd/3.0/ie/