Personalized app recommendation using spatio-temporal app usage log

Abstract

This paper proposes a probabilistic method to recommend apps appropriate to current time and location of a user. The proposed method regards an app as a distribution of topics discovered from a large number of app descriptions. A user preference is then modeled, using spatio-temporal app usage log of a user, as a topic distribution that is affected by time and location. Since time and location can be regarded as two continuous random variables that are independent of each other, the proposed method is in contrast to conventional methods in that the conventional methods are based on a limited number of discrete contexts and assume that locations are dependent on time. Therefore, the proposed method captures user-specific contexts and is robust even in unseen time and location. Our experiments show that the proposed method outperforms two baseline methods in NDCG, which implies that the proposed method is effective in personalized app recommendation.