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Title: k-Hit Quer y: Top-k Query with Probabilistic Utility Function
Citation Type: Conference Paper
Publication Year: 2015
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Abstract: Multi-criteria decision making problem has been well studied for many years. One popular query for multi-criteria decision making is top-k queries which require each user to specify an exact utility function. In many cases, the utility function of a user is probabilistic and finding the distribution on the utility functions has been widely explored in the machine learning areas, such as users recommender systems, Bayesian learning models and users preference elicitation, for improving users experience. Motivated by this, we propose a new type of queries called k-hit queries, which has not been studied before. Given a set D of tuples in the database, the distribution on utility functions and a positive integer k, we would like to select a set of k tuples from D in order to maximize the probability that at least one of tuples in the selection set is the favorite of a user. All applications for top-k queries can naturally be used in k-hit queries. In this paper, we present various interesting properties of k-hit queries. Besides, based on these properties, we propose a novel algorithm called k-hit_Alg for k-hit queries. Finally, we conducted comprehensive experiments to show that the performance of our proposed method, k-hit_Alg, is superior compared with other existing algorithms which were originally used to answer other existing queries.
Url: http://www.cse.ust.hk/~ppeng/papers/SIGMOD15_conf_full_213.pdf
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Authors: Peng, Peng; Wong, Raymond Chi-Wing
Conference Name: SOGMOD '15
Publisher Location: Melbourne, Australia
Data Collections: IPUMS USA
Topics: Other
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