Recommending collaboration with social networks: a comparative evaluation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Searching for experts in the enterprise: combining text and social network analysis
Proceedings of the 2007 international ACM conference on Supporting group work
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Finding similar users in social networks: extended abstract
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Trajectory simplification method for location-based social networking services
Proceedings of the 2009 International Workshop on Location Based Social Networks
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Circle of friend query in geo-social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Mining user similarity based on routine activities
Information Sciences: an International Journal
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The online social network services have been growing rapidly over the past few years, and the social network services can easily obtain the locations of users with the recent increasing popularity of the GPS enabled mobile device. In the social network, calculating the similarity between users is an important issue. The user similarity has significant impacts to users, communities and service providers by helping them acquire suitable information effectively. There are numerous factors such as the location, the interest and the gender to calculate the user similarity. The location becomes a very important factor among them, since nowadays the social network services are highly coupled with the mobile device which the user holds all the time. There have been several researches on calculating the user similarity. However, most of them did not consider the location. Even if some methods consider the location, they only consider the physical location of the user which cannot be used for capturing the user's intention. We propose an effective method to calculate the user similarity using the semantics of the location. By using the semantics of the location, we can capture the user's intention and interest. Moreover, we can calculate the similarity between different locations using the hierarchical location category. To the best of our knowledge, this is the first research that uses the semantics of the location in order to calculate the user similarity. We evaluate the proposed method with a real-world use case: finding the most similar user of a user. We collected more than 251,000 visited locations over 591 users from foursquare. The experimental results show that the proposed method outperforms a popular existing method calculating the user similarity.