关键词: 年龄预测, 隐式反馈, 推荐系统, 矩阵分解

Abstract:

With the rapid development of mobile devices, and the number of mobile applications has grown very rapidly, how to recommend appropriate mobile applications which users are really interested in becomes more and more important. It is found that user's age is one of the factors that can affect the choice of mobile Apps of users, after analyzing some questionnaires and the mobile industry reports released by Airei. Based on this result, this paper proposes to integrate user's age information into traditional recommendation algorithms for personalized mobile application recommendation. The prediction of user's age is regarded as a classification problem. It uses multi-view learning approach to expand training dataset based on questionnaire and user behavior information. Then user age prediction model can be built through classification model based on user's historical behavior information. This paper proposes two recommendation models: AgeBPR and AgeSocialMF, to incorporate age information to traditional recommendation models. Experiments conducted on a large number of user's real behavior data show that the proposed models AgeBPR and AgeSocialMF gain a higher recommendation accuracy compared with models without using age information, suggesting that the proposed strategy, integrating user's age information into traditional recommendation models, is effective to improve the recommendation accuracy.

Key words: age prediction, implicit feedback, recommendation system, matrix factorization