Mobile Social Software: Facilitating Serendipity or Encouraging Homogeneity?
IEEE Pervasive Computing
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
SPETA: Social pervasive e-Tourism advisor
Telematics and Informatics
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Investigating Homophily in Online Social Networks
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Smart itinerary recommendation based on user-generated GPS trajectories
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Implementation of CAMEO: A context-aware middleware for Opportunistic Mobile Social Networks
WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
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By leveraging location data in online social networks, Location-based Social Networks (LBSNs) can support diverse human activities such as tourism. Different applications aim to aid tourists and provide better experience in their travels by matching co-located users based on what they have in common. However, users with little in common but with potential to help each other given the context and place could not be matched. In this paper we introduce traMSNet, a LBSN that implements a matching algorithm considering homophily, as well as users complementary skills in a touristic location. Our idea is validated with a survey that asked potential travelers about their needs when looking for a travel partner. Moreover, we present a matching algorithm that is evaluated it with real tourists. The evaluation shows that considering complementarity when matching individuals is preferred by users. Therefore, by only considering similarities, important issues are left aside.