Inferring contextual preferences using deep auto-encoding

Moshe Unger, Bracha Shapira, Lior Rokach, Ariel Bar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationUMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages221-229
Number of pages9
ISBN (Electronic)9781450346351
DOIs
StatePublished - 9 Jul 2017
Externally publishedYes
Event25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017

Publication series

NameUMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Conference

Conference25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
Country/TerritorySlovakia
CityBratislava
Period9/07/1712/07/17

Keywords

  • Auto-encoder
  • Context
  • Deep learning
  • Mobile
  • User profiling

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