Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supporting social navigation on the World Wide Web
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
A music recommender based on audio features
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System
IEEE Intelligent Systems
Organizing the unorganized - employing IT to empower the under-privileged
Proceedings of the 17th international conference on World Wide Web
Avaaj Otalo: a field study of an interactive voice forum for small farmers in rural India
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering recommender systems
The adaptive web
User driven audio content navigation for spoken web
Proceedings of the international conference on Multimedia
Data-model for voice search of agricultural information system
Proceedings of the first workshop on Information and knowledge management for developing region
From sensing to controlling: the state of the art in ubiquitous crowdsourcing
International Journal of Communication Networks and Distributed Systems
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This paper describes our experiences deploying a recommender system for a mobile phone-based knowledge sharing application to farmers in rural India. Users of the system record questions and call back for answers left by other users and experts. We used collaborative filtering to derive relevant content for each user based on historical navigation patterns of the community. An empirical analysis of behavioral and interview data reveals key issues for future mobile recommender systems in developing regions of the world.