DocumentCode
2783686
Title
A user selection method in advertising recommendations
Author
Wu, Xiaoli ; Xiao, Bo ; Lin, Zhiqing
Author_Institution
Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear
2009
fDate
6-8 Nov. 2009
Firstpage
410
Lastpage
413
Abstract
User recommendation problem is important for mobile operators when they provide some new service to users. The traditional methods provide a low success rate. In this paper, we present a novel user selection method of advertising recommendation according to the maximal frequent items discovery theory. The experimental results demonstrate that our method can improve the success rate dramatically and reduce the amount of garbage advertisements.
Keywords
advertising; customer services; recommender systems; advertising recommendations; garbage advertisements; maximal frequent items discovery theory; mobile operators; user selection method; Advertising; Collaboration; Electronic commerce; Feedback; Information filtering; Information filters; Intelligent systems; Itemsets; Monitoring; Pattern recognition; Classification; Maximum frequent itemset; Recommendation system; User selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-4898-2
Electronic_ISBN
978-1-4244-4900-6
Type
conf
DOI
10.1109/ICNIDC.2009.5360981
Filename
5360981
Link To Document