Let's browse: a collaborative Web browsing agent
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Understanding and Using Context
Personal and Ubiquitous Computing
Models of attention in computing and communication: from principles to applications
Communications of the ACM
Personalized location-based brokering using an agent-based intermediary architecture
Decision Support Systems - Special issue: Agents and e-commerce business models
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Using self-defined group activities for improvingrecommendations in collaborative tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Towards a group recommender process model for ad-hoc groups and on-demand recommendations
Proceedings of the 16th ACM international conference on Supporting group work
Space efficiency in group recommendation
The VLDB Journal — The International Journal on Very Large Data Bases
AGReMo: providing ad-hoc groups with on-demand recommendations on mobile devices
Proceedings of the 29th Annual European Conference on Cognitive Ergonomics
Intelligent Decision Technologies - Special issue on Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
A group recommendation approach for service selection
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Since 1990s, with an advancement of network technology and the popularization of the Internet, information that people can access has proliferated, thus information recommendation has been investigated as an important issue. Because preference to information recommendation can be different as context that the users are related to, we should consider this context to provide a good service. This paper proposes the recommendation system that considers the preferences of group users in mobile environment and applied the system to recommendation of restaurants. Since mobile environment has plenty of uncertainty, our system have used Bayesian network which showed reliable performance with uncertain input to model individual user's preference. Also, restaurant recommendation mostly considers the preference of group users, so we have used AHP (Analytic Hierarchy Process) of multi-criteria decision making method to get the preference of group users from individual users' preferences. For experiments, we have assumed 10 different situations and compared the proposed method with random recommendation and simple rule-based recommendation. Finally, we have confirmed that the proposed system provides high usability with SUS (System Usability Scale).