CAMER: A Context-Aware Mobile Service Recommendation System

The increasing number of mobile services makes users confused to select appropriate services among plenty of service icons or links. Current developers always choose to recommend recently or mostly used services to users, but these approaches neglect the relations between user states and environment information and invocations, and the recommendation results will not be accurate when the mobile services are invoked evenly. In this paper, we propose a novel approach to recommend services on mobile devices to user. Firstly, we design a user behavior model by taking advantage of user's mobile context information like time and location to describe the user states. Secondly, we design a generate model to explain how the sequential service invocations are generated by analyzing the collected sequential history record of mobile users. Thirdly, we adopt logistic model tree approach to determine user state according to given mobile context information, and recommend services to user according to his user state. The experiment results show that our approach performs better than baseline approaches.