How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough
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Date
2017-06-06T02:11:13Z
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Abstract
One of the modern pillars of collaborative filtering and recommender systems is collection and exploitation of ratings from users. Likert scale is a psychometric quantifi er of ratings popular among the electronic commerce sites. In this paper, we consider the tasks of collecting Likert scale ratings of items and of fi nding 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. We empirically and comparatively evaluate with real data sets and discuss the effectiveness and efficiency of our and competing approaches. Our method is effective and competitively efficient.
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 and recommends how many additional ratings should be proactively
sought. We also empirically evaluate with real data sets the effectiveness of our method to recommend the collection of additional ratings. The results show that the approach is practical and effective.