Research on Personalized Recommendation of Smart Phones Applications Based on Association Rules and Collaborative Filtering Algorithms

Rapid growth in the number of smart phones users brings endless opportunities and enormous challenges for mobile Internet applications. How to recommend applications in which users are interested draws attentions of the application stores. In this paper, the features of different categories applications are extracted by analyzing users behavior of downloading. Based on association rules, the recommendations of overall and different categories applications can be generated respectively, as well as the recommendations of applications that are most likely to be accepted by users based on collaborative filtering. In this method, two recommendation strategies are proposed: the triggered recommendation strategy which is based on the optimum number of the application in the user’s smart phone and the automatic recommendation strategy which is based on the evaluation of users’ historical performance. The experiment result demonstrates the feasibility and effectivity of these recommendation strategies.