TY - GEN
T1 - How to find the best rated items on a likert scale and how many ratings are enough
AU - Liu, Qing
AU - Basu, Debabrota
AU - Goel, Shruti
AU - Abdessalem, Talel
AU - Bressan, Stéphane
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-64471-4_28
DO - 10.1007/978-3-319-64471-4_28
M3 - Conference contribution
AN - SCOPUS:85028459160
SN - 9783319644707
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 351
EP - 359
BT - Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Proceedings
A2 - Damiani, Ernesto
A2 - Sheth, Amit
A2 - Grosky, William I.
A2 - Hameurlain, Abdelkader
A2 - Benslimane, Djamal
A2 - Wagner, Roland R.
PB - Springer Verlag
T2 - 28th International Conference on Database and Expert Systems Applications, DEXA 2017
Y2 - 28 August 2017 through 31 August 2017
ER -