Deep auto-encoding for context-aware inference of preferred items' categories

Context-aware systems enable the sensing and analysis of user context in order to provide personalized services to users. We observed that it is possible to automatically learn contextual factors and behavioral patterns when users interact with the system. We later utilize the learned patterns to infer contextual user interests within a recommender system. We present a novel context-aware model for detecting users' preferred items' categories using an unsupervised deep learning technique applied to mobile sensor data. We train an auto-encoder for each item genre, using contextual data that was obtained when users interacted with the system. Given new contextual sensor data from a user, the discovered patterns from each auto-encoder are used to predict the category of items that should be recommended to the user in the given context. In order to collect rich contextual data, we conducted an extensive field study over a period of four weeks with a group of ninety users. The analysis reveals significant insights regarding the inference of different granularity levels of categories that are available within the data.