User preference space partition and product filters for reverse top-k queries

Zong Hua Yang, Hung-Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Top-k queries have been studied mainly from the perspective of the user. Many researchers have focused on improving the efficiency of top-k problems. However, few studies have focused on the essential factors required for manufacturers to assess the potential market. A novel query type, namely, the reverse top-k, is used to assess the potential market and help manufacturers calculate the impact of their products. Given a potential product, reverse top-k will find the user preferences for which this product is in the top-k query result set. Although several algorithms can solve the reverse top-k problem, none that are available can solve the reverse top-k problem when the number of products or users is large. In this paper, we formally define our algorithm as FSP (filtering and space partition) and explain how FSP solves the reverse top-k problem. The main idea of FSP is to use the partition of the candidate space to reduce the searching of space for products. In our experimental results, FSP can find the same results as other algorithms, but FSP reduces the time cost from 231 msec to 32 msec.

Original languageEnglish
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages498-504
Number of pages7
ISBN (Electronic)9781479969913
DOIs
Publication statusPublished - 2014 Mar 10
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: 2014 Oct 302014 Nov 1

Publication series

NameDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Other

Other2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
CountryChina
CityShanghai
Period14-10-3014-11-01

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management

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