GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Recent advances and future directions in multimedia and mobile computing
Multimedia Tools and Applications
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Mobile devices need to provide more accurate and personalized information in a computing environment with a small screen and limited information retrieval functions. This paper presents a user-selectable recommendation system that reflects a user interest group by employing collaborative filtering as technique to provide useful information in a mobile environment. We form similar groups by simultaneously considering a user's information preferences and demographics. Then we reconstruct lists of a final recommendation based on what search results the similar demographic group has chosen. This is an optional filter for the search results. This means that we provide an interactive flexible recommendation list that considers a user's intent more actively, rather than unilaterally. We show the Mean Absolute Error result to evaluate the recommendation and finally show the realization of a prototype that is based on both the iPhone and Android phone environments.