Title :
Research on personalized recommendation model for mobile advertising
Author :
Gu Qi-wei ; Guo Peng
Author_Institution :
Coll. of Manage., Shenzhen Univ., Shenzhen, China
Abstract :
Aimed at enhancing the accuracy of the personalized recommendation for mobile advertising, overcoming the shortcomings of the traditional similarity calculation and collaborative filtering recommendation techniques, the cloud model calculation method improved the strict item or project matching problem in traditional similarity calculation, resolved extreme sparse data problem. And a mixed recommendation model is established based on mobile advertisement, content recommendation and linear combination of collaborative filtering recommendation. Experiments prove that the new method has obviously smaller MAE and higher quality in recommendation system.
Keywords :
advertising; cloud computing; collaborative filtering; mobile computing; recommender systems; cloud model calculation method; collaborative filtering recommendation techniques; content recommendation; extreme sparse data problem; linear combination; mixed recommendation model; mobile advertisement; mobile advertising; personalized recommendation model; project matching problem; similarity calculation; strict item; Advertising; Collaboration; Computational modeling; Educational institutions; Filtering; Mobile communication; Predictive models; E-commerce; cloud model; mixed recommendation; mobile advertising; recommendation system;
Conference_Titel :
Management Science and Engineering (ICMSE), 2012 International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-3015-2
DOI :
10.1109/ICMSE.2012.6414161