Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Mining user similarity from semantic trajectories
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
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Researches on recommending followees in social networks have attracted a lot of attentions in recent years. Existing studies on this topic mostly treat this kind of recommendation as just a type of friend recommendation. However, apart from making friends, the reason of a user to follow someone in social networks is inherently to satisfy his/her information needs in asymmetrical manner. In this paper, we propose a novel mining-based recommendation approach named Geographic-Textual-Social Based Followee Recommendation (GTS-FR), which takes into account the user movements, online texting and social properties to discover the relationship between users' information needs and provided information for followee recommendation. The core idea of our proposal is to discover users' similarity in terms of all the three properties of information which are provided by the users in a Location-Based Social Network (LBSN). To achieve this goal, we define three kinds of features to capture the key properties of users' interestingness from their provided information. In GTS-FR approach, we propose a series of novel similarity measurements to calculate similarity of each pair of users based on various properties. Based on the similarity, we make on-line recommendation for the followee a user might be interested in following. To our best knowledge, this is the first work on followee recommendation in LBSNs by exploring the geographic, textual and social properties simultaneously. Through a comprehensive evaluation using a real LBSN dataset, we show that the proposed GTS-FR approach delivers excellent performance and outperforms existing stat-of-the-art friend recommendation methods significantly.