Mining mobility data to minimise travellers' spending on public transport
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives
Proceedings of the 13th international conference on Ubiquitous computing
Effective event discovery: using location and social information for scoping event recommendations
Proceedings of the fifth ACM conference on Recommender systems
Geo-social recommendations based on incremental tensor reduction and local path traversal
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
FunSquare: first experiences with autopoiesic content
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Exploiting space-time status for service recommendation
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Identifying users profiles from mobile calls habits
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Spotting trends: the wisdom of the few
Proceedings of the sixth ACM conference on Recommender systems
Ads and the city: considering geographic distance goes a long way
Proceedings of the sixth ACM conference on Recommender systems
Middleware for pervasive computing: A survey
Pervasive and Mobile Computing
Mining user similarity based on routine activities
Information Sciences: an International Journal
Geo-spotting: mining online location-based services for optimal retail store placement
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid event recommendation using linked data and user diversity
Proceedings of the 7th ACM conference on Recommender systems
Buffer occupation in wireless social networks
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
A weight-aware recommendation algorithm for mobile multimedia systems
Mobile Information Systems
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A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts, recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location? To answer this question, we carry out a study of the relationship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.