A Collaborative Filtering Recommendation Algorithm Based on Context Similarity for Mobile Users
Xu Feng-ling Meng Xiang-wu Wang Li-cai
(Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China)
(School of Computer, Beijing University of Posts and Telecommunications, Beijing 100876, China)
Abstract：Towards the problem of personalized services recommendation in mobile telecommunication network, a collaborative filtering algorithm based on context similarity for mobile users is proposed by incorporating mobile users’ context information into collaborative filtering recommendation process. The algorithm calculates firstly user-based context similarities to construct a set of similar contexts related to the current context of the active user. Then it reduces the “mobile user-mobile service-context” 3D model to the “mobile user-mobile service” 2D model by using context pre-filtering recommendation method. Finally it predicts the unknown user preferences and generates recommendations based on the traditional 2D Collaborative Filtering (CF) algorithm. Experimental results indicate that this algorithm can be applied to predict user preferences in mobile network service environment and achieve better recommendation accuracy than the traditional CF algorithm.
徐风苓, 孟祥武, 王立才. 基于移动用户上下文相似度的协同过滤推荐算法[J]. 电子与信息学报, 2011, 33(11): 2785-2789.
Xu Feng-Ling, Meng Xiang-Wu, Wang Li-Cai. A Collaborative Filtering Recommendation Algorithm Based on Context Similarity for Mobile Users. , 2011, 33(11): 2785-2789.
2011, Vol. 33 
