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- W4282015265 abstract "Abstract Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasising personalisation through the optimisation of accuracy-focused metrics, textit{over}-textit{personalisation} may emerge, with negative effects on the user experience. A countermeasure to the problem is to diversify recommendations. In this paper, we present a solution that addresses the problem for three application domains: movies, books, and music. Our solution, which we call textit{VarSeek}, models the consumption diversity vs. neighbour similarity trade-off to control diversity and observe its impact on four related dimensions, namely item diversity, local coverage, novelty, and redundancy. The proposed solution is designed to diversify user profiles built on categorical preferences in a collaborative filtering scenario. We perform offline evaluations against state-of-the-art methods on three widely used datasets and show that the proposed algorithm is able to control recommendation novelty and redundancy." @default.
- W4282015265 created "2022-06-13" @default.
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- W4282015265 date "2022-05-31" @default.
- W4282015265 modified "2023-10-16" @default.
- W4282015265 title "Exploiting Consumption Diversity and neighbour Similarity Trade-offs in Recommender Systems: a User-Centric Offline Evaluation of Diversity Objectives" @default.
- W4282015265 doi "https://doi.org/10.21203/rs.3.rs-1692543/v1" @default.
- W4282015265 hasPublicationYear "2022" @default.
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