Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
QoS-Aware Web Service Recommendation by Collaborative Filtering
IEEE Transactions on Services Computing
Task knowledge based retrieval for service relevant to mobile user's activity
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
From sensing to controlling: the state of the art in ubiquitous crowdsourcing
International Journal of Communication Networks and Distributed Systems
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Recently, it has become very hard for users to find their desired mobile services because the number of applications and Web services are rapidly increasing. Therefore, it is important to realize context-aware application recommendation. Because it is necessary to collect large learning data to estimate user's context, we propose a platform for collecting users' context and relationship between context and application by providing an application search system that inquires user's current context. We implemented a system named "App.Locky" based on our proposal and conducted experiments by publishing the system on the internet. As a result, we confirmed that collected search logs can be used to estimate user's context and relationship between context and application.