Bayesian Personalized Ranking for Novelty Enhancement

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Title: Bayesian Personalized Ranking for Novelty Enhancement
Authors: Wasilewski, JacekHurley, Neil J.
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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 systemsInformation retrievalRetrieval models and rankingRetrieval tasks and goalsNovelty in information retrieval
DOI: 10.1145/3320435.3320468
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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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