Recommending Social Events from Mobile Phone Location Data

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.

Mapping collective human activity in an urban environment based on mobile phone data

“…Research on such dynamics on the basis of mobile phone data has been intensively conducted by the MIT's SENSEable City Lab (http://senseable.mit.edu/) and partner institutions (Krings et al 2009;Quercia et al 2010;Calabrese, Colonna, et al 2011;Calabrese, Smoreda, et al 2011;Di Lorenzo and Calabrese 2011;Calabrese et al 2013). Such data-driven studies explore diverse characteristics of urban environments -from general urban activity patterns (Ratti et al 2006;) to individual mobility preferences (Calabrese et al 2013) or characteristics of group behavior (Farrahi and Gatica-Perez 2011).…”

Practical Implementation of Mobile-based Online ILFS Resources Evaluation

“…Some of the common applications of LBS are, acquiring about the nearest business, recommending social events, finding people on a map, turn by turn navigation [2]. As the word "location" indicates, LBS require accurate positioning (the words location and position are used interchangeably in this paper as they both refer to the coordinates of a target) to be completely functional.…”

Recommendation System for Web Mining: A Review

“…In the author describe a technique where a geographical location can be traced on the basis of user movements [9] and Helping user to selecting the traveling behavior, by use of available online information. A touristic-area-season modeled and developed in social event [10].…”

“…In the ere a geographical location can be traced on the basis of user movements [9] and Helping user to selecting the traveling behavior, by use of available online season modeled and developed in social event [10]. twitter also use TWITOBI a recommendation system for suggest topk-user follow and topk tweet to read web page recommendation [11] can be described as a application in which a graph based approach is used to web mining and its type, techniques.…”