Inferring Contextual Preferences Using Deep Auto-Encoding Conference Paper uri icon


  • 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 high-dimensional 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) recommende...

publication date

  • January 1, 2017