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
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;
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
DOI :
10.1109/ICNIDC.2009.5360981
