Inferring contextual preferences using deep auto-encoding

Context-Aware systems enable the sensing and analysis of user context in order to provide personalized services. Our study is part of growing research efforts examining how highdimensional data collected from mobile devices can be utilized to infer users' dynamic preferences. We present a novel method for inferring contextual user preferences by using an unsupervised deep learning technique applied to mobile sensor data. We train an auto-encoder for each user preference with contextual data that based on past user interaction with the system. Given new contextual sensor data from a user, the patterns discovered from each auto-encoder are used to predict the most likely preference in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-Aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual preferences based on the interaction of users with categories of items. Empirical results utilizing a real world dataset of mobile users show a significant improvement (16% to 73% improvement) in classification accuracy compared with state of the art classification methods.