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Truthfulness of Candidates in Set of t-uples Expansion

Abstract : The collection and exploitation of ratings from users are modern pillars of collaborative filtering. Likert scale is a psychometric quantifier of ratings popular among the electronic commerce sites. In this paper, we consider the tasks of collecting Likert scale ratings of items and of finding the n-k best-rated items, i.e., the n items that are most likely to be the top-k in a ranking constructed from these ratings. We devise an algorithm, Pundit, that computes the n-k best-rated items. Pundit uses the probability-generating function constructed from the Likert scale responses to avoid the combinatorial exploration of the possible outcomes and to compute the result efficiently. Selection of the best-rated items meets, in practice, the major obstacle of the scarcity of ratings. We propose an approach that learns from the available data how many ratings are enough to meet a prescribed error. We empirically validate with real datasets the effectiveness of our method to recommend the collection of additional ratings.
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Submitted on : Friday, February 2, 2018 - 11:36:15 PM
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Ngurah Agus Sanjaya Er, Mouhamadou Lamine Ba, Talel Abdessalem, Stéphane Bressan. Truthfulness of Candidates in Set of t-uples Expansion. Proc. of the 28th International Conference on Database and Expert Systems Applications (DEXA 2017), Aug 2017, Lyon, France. pp.314-323, ⟨10.1007/978-3-319-64468-4_24⟩. ⟨hal-01699982⟩



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