SocialCRC: a social- and context-aware rendezvous coordination system
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Hybrid-ε-greedy for mobile context-aware recommender system
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Exploration / exploitation trade-off in mobile context-aware recommender systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Knowledge-Based Systems
Improving contextual advertising by adopting collaborative filtering
ACM Transactions on the Web (TWEB)
International Journal of Advanced Pervasive and Ubiquitous Computing
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Mobile-enabled social networks applications are becoming increasingly popular. Most of the current social network applications have been designed for high-end mobile devices, and they rely upon features such as GPS, capabilities of the world wide web, and rich media support. However, a significant fraction of mobile user base, especially in the developing world, own low-end devices that are only capable of voice and short text messages (SMS). In this context, a natural question is whether one can design meaningful social network-based applications that can work well with these simple devices, and if so, what the real challenges are. Towards answering these questions, this paper presents a social network-based recommender system that has been explicitly designed to work even with devices that just support phone calls and SMS. Our design of the social network based recommender system incorporates three features that complement each other to derive highly targeted ads. First, we analyze information such as customer's address books to estimate the level of social affinity among various users. This social affinity information is used to identify the recommendations to be sent to an individual user. Second, we combine the social affinity information with the spatio-temporal context of users and historical responses of the user to further refine the set of recommendations and to decide when a recommendation would be sent. Third, social affinity computation and spatio-temporal contextual association are continuously tuned through user feedback. We outline the challenges in building such a system, and outline approaches to deal with such challenges.