Personalised Ranking with Diversity

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Title: Personalised Ranking with Diversity
Authors: Hurley, Neil J.
Permanent link: http://hdl.handle.net/10197/10880
Date: 16-Oct-2013
Online since: 2019-07-11T08:24:38Z
Abstract: In this paper we discuss a method to incorporate diversity into a personalised ranking objective, in the context of ranking-based recommendation using implicit feedback. The goal is to provide a ranking of items that respects user preferences while also tending to rank diverse items closely together. A prediction formula is learned as the product of user and item feature vectors, in order to minimise the mean squared error objective used previously in the RankALS and RankSGD methods, but modified to weight the difference in ratings between two items by the dissimilarity of those items. We report on preliminary experiments with this modified objective, in which the minimisation is carried out using stochastic gradient descent. We show that rankings based on the output of the minimisation succeed in producing recommendation lists with greater diversity, with just a small loss in relevance of the recommendation, as measured by the error rate.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Start page: 379
End page: 382
Copyright (published version): 2013 ACM
Keywords: Recommender SystemsDiversityImplicit ratings
Other versions: http://recsys.acm.org/recsys13/
Language: en
Status of Item: Peer reviewed
Is part of: Proceeding RecSys '13 Proceedings of the 7th ACM conference on Recommender systems
Conference Details: RecSys '13: 7th ACM conference on Recommender systems, Hong Kong, China, 12-16 October 2013
ISBN: 978-1-4503-2409-0
Appears in Collections:Insight Research Collection

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