TY - GEN
T1 - User preference space partition and product filters for reverse top-k queries
AU - Yang, Zong Hua
AU - Kao, Hung-Yu
PY - 2014/3/10
Y1 - 2014/3/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84946693547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946693547&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2014.7058118
DO - 10.1109/DSAA.2014.7058118
M3 - Conference contribution
AN - SCOPUS:84946693547
T3 - DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
SP - 498
EP - 504
BT - DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
A2 - Karypis, George
A2 - Cao, Longbing
A2 - Wang, Wei
A2 - King, Irwin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
Y2 - 30 October 2014 through 1 November 2014
ER -