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- W2113885898 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." @default.
- W2113885898 created "2016-06-24" @default.
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- W2113885898 date "2013-10-12" @default.
- W2113885898 modified "2023-09-23" @default.
- W2113885898 title "Personalised ranking with diversity" @default.
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- W2113885898 doi "https://doi.org/10.1145/2507157.2507226" @default.
- W2113885898 hasPublicationYear "2013" @default.
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