A Collaborative Filtering Recommendation Algorithm Based on Context Similarity for Mobile Users

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.