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- Exploiting Behavioral User Models in Smart Environments
- UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization
- Book/source title
- Book/source subtitle
- adjunct publication of the 25th Conference on User Modeling, Adaptation and Personalization : July 9-12, 2017, Bratislava, Slovakia
- Pages (from-to)
- The Association for Computing Machinery
- ISBN (electronic)
- Document type
- Conference contribution
- Interfacultary Research Institutes
Faculty of Humanities (FGw)
- Institute for Logic, Language and Computation (ILLC)
There is a growing interest in using the Internet of Things (IoT) to create smart environments, which hold the promise to provide personalized experience based on the trail of user interactions with smart devices. We experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides. We have logged users' onsite physical information interactions of visits in a museum. Moreover, to have a better understanding of users' information interaction behaviors and their preferences, we have collected and studied query logs of a search engine of the same collection, and we have found similarities between users' online digital and onsite physical information interaction behaviors. We exploit user modeling based on users' different information interaction behaviors and experiment with a novel approach to a critical one-shot POI recommendation using deep neural multilayer perceptron based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Experimental results indicates that our proposed behavioral user modeling, using both physical and digital user information interaction behaviors, improves the onsite POI recommendation baselines' performances in all common Information Retrieval evaluation metrics. Our proposed approach provides an effective way to achieve a high precision at rank 1 in onsite critical one-shot POI recommendation problem.
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