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Recommending Social Events from Mobile Phone Location Data

Quercia, D.; Lathia, N.; Calabrese, F.; Di Lorenzo, G.; Crowcroft, J.; (2010) Recommending Social Events from Mobile Phone Location Data. In: ICDM 2010 The 10th IEEE International Conference on Data Mining 14-17 December 2010 Sydney, Australia. (pp. pp. 971-976). IEEE: Los Alamitos, CA US. Green open access

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Abstract

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.

Type:Proceedings paper
Title:Recommending Social Events from Mobile Phone Location Data
ISBN:978-1-4244-9131-5
Open access status:An open access version is available from UCL Discovery
DOI:10.1109/ICDM.2010.152
Publisher version:http://dx.doi.org/10.1109/ICDM.2010.152
Language:English
Additional information:©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:Mobile, recommender systems, web 2.0
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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