Bayesian Personalized Ranking for Novelty Enhancement
|Title:||Bayesian Personalized Ranking for Novelty Enhancement||Authors:||Wasilewski, Jacek; Hurley, Neil J.||Permanent link:||http://hdl.handle.net/10197/10834||Date:||12-Jun-2019||Online since:||2019-07-02T07:06:22Z||Abstract:||Novelty 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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Start page:||144||End page:||148||Copyright (published version):||2019 Association for Computing Machinery||Keywords:||Recommender systems; Information retrieval; Retrieval models and ranking; Retrieval tasks and goals; Novelty in information retrieval||DOI:||10.1145/3320435.3320468||Other versions:||http://www.cyprusconferences.org/umap2019/||Language:||en||Status of Item:||Peer reviewed||Is part of:||UMAP '19 Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization||Conference Details:||UMAP '19: 27th ACM Conference on User Modeling, Adaptation and Personalization, Larnaca, Cyprus, 9–12 June 2019||ISBN:||978-1-4503-6021-0|
|Appears in Collections:||Insight Research Collection|
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