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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Acknowledgement
This work is supported by the National University of Singapore under a grant from Singapore Ministry of Education for research project number T1 251RES1607 and is partially funded by the Big Data and Market Insights Chair of Télécom ParisTech.
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Liu, Q., Basu, D., Goel, S., Abdessalem, T., Bressan, S. (2017). How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_28
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DOI: https://doi.org/10.1007/978-3-319-64471-4_28
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