Integration of Heterogeneous Web Services for Event-Based Social Networks

Event-based online social networks are Internet-based services that enable users to participate in real world experiences together. Event-based social networks can be created by a community of end-users based on their own interests in specific types of event and sources of event information. We propose a method to create such event-based social networks through integration of existing online information sources of events using a Semantic Web framework. In order to match people with common interests in such activities to self-organize into a social network, we integrate information from heterogeneous information sources related to event schedules, ticket purchases, and group attendance from multiple online sources. The Semantic Web framework is used to represent these heterogeneous datasets and unstructured online data is converted into ontologies. Links between event information in different sources are discovered using both the syntactic similarity and semantic similarity between ontology classes. We use an approach based on Latent Dirichlet Allocation (LDA) over the space of topics related to each event and user profiles for event recommendation. This enables the event-based social network to recommend friends based on shared interest in an event - online friendship is established after mutual attendance of the same event. We demonstrate this approach with EasyGo, a web-based mashup application which integrates information of events such as concerts, sports, theatres, as well as tickets and group purchase from multiple online sources.

[1]  Ziqi Wang,et al.  Graph-Based Recommendation on Social Networks , 2010, 2010 12th International Asia-Pacific Web Conference.

[2]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[3]  V. Prasanna,et al.  Event Recommendation in Social Networks with Linked Data Enablement , 2013, ICEIS.

[4]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[5]  Yuanyuan Tian,et al.  Event-based social networks: linking the online and offline social worlds , 2012, KDD.

[6]  Jeffrey Fekete.  Making the big game : tales of an accidental spectator , 2009 .

[7]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[8]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[9]  Soon Ae Chun,et al.  Social Data Integration and Analytics for Health Intelligence , 2014 .

[10]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[11]  Li Guo,et al.  Event Recommendation in Event-Based Social Networks , 2014, AAAI.

[12]  Wei Zhang,et al.  Combining latent factor model with location features for event-based group recommendation , 2013, KDD.

[13]  Hao Wu,et al.  EasyGo : An Event-based Social Network , 2013 .

[14]  Li Guo,et al.  Combining geographical information of users and content of items for accurate rating prediction , 2014, WWW '14 Companion.