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• W2818811943 abstract "In this study, we address the challenge of measuring the ability of a recommender system to make surprising recommendations. Although current evaluation methods make it possible to determine if two algorithms can make recommendations with a significant difference in their average measure, it could be of interest to our community to know how competent an algorithm is at embedding in its recommendations, without having to resort to making a direct comparison with another algorithm. We argue that a) is a finite resource in a recommender system, b) there is a limit to how much any algorithm can embed in a recommendation, and c) this limit can provide us with a scale against which the performance of any algorithm can be measured. By exploring these ideas, it is possible to define the concepts of maximum and minimum potential and design a metric called normalised surprise that employs these limits to potential surprise. Two experiments were conducted to test the proposed metric. The aim of the first was to validate the quality of the estimates of minimum and maximum potential produced by a greedy algorithm. The purpose of the second experiment was to analyse the behaviour of the proposed metric using the MovieLens dataset. The results confirmed the behaviour that was expected, and showed that the proposed metric is both effective and consistent for differing choices of recommendation algorithms, data representations and distance functions." @default.
• W2818811943 created "2018-07-19" @default.
• W2818811943 creator A5049168316 @default.
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• W2818811943 date "2019-07-04" @default.
• W2818811943 modified "2023-09-23" @default.
• W2818811943 title "Limits to surprise of recommender systems" @default.
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• W2818811943 doi "https://doi.org/10.11606/d.100.2019.tde-15042019-175412" @default.
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