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Personalised Ranking with Diversity
Author(s)
Date Issued
2013-10-16
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Start Page
379
End Page
382
Copyright (Published Version)
2013 ACM
Web versions
Language
English
Status of Item
Peer reviewed
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
This item is made available under a Creative Commons License
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