Personalized location recommendation using mobile phone usage information

Abstract

Location recommendation has become a hot research area in recent years. The cold-start problem is still a great challenge in personalized location recommendation, which makes it difficult to infer a new user’s preferences, because a new user generally has never visited any location at the start. To address this problem, the existing studies usually exploit other information, e.g., demographic features, to characterize users. However, such little information is not sufficient to profile users accurately. In addition, abundant mobile phone usage information can be recorded when users are using their phones, e.g., the use frequency of Apps, which can fully reveal the diverse characteristics of different users. In this paper, we propose a personalized location recommendation method using mobile phone usage information, which transforms the location recommendation problem into a regression task, and extracts six types of mobile phone usage features to profile users. Demographic features and location features are also extracted. To efficiently reduce model parameters, factorization machines are employed to construct the recommendation model, which models feature interactions as the inner products of latent vectors with matrix factorization. We evaluate the proposed method using the open dataset of Nokia Mobile Data Challenge, and experimental results show the effectiveness of the proposed method in personalized location recommendation.