Event Recommendation in Social Networks with Linked Data Enablement
Yinuo Zhang, Hao Wu, Vikram Sorathia, Viktor K. Prasanna · 2013
In recent years, social networking services have gained phenomenal popularity. They allow us to explore the world and share our findings in a convenient way. Event is a critical component in social networks. A user can create, share or join different events in their social circle. In this paper, we investigate the problem of event recommendation. We propose recommendation methods based on the similarity of an event’s content and a user’s interests in terms of topics. Specifically, we use Latent Dirichlet Allocation (LDA) to generate a topic distribution over each event and user. We also consider friend relationship and attendance history to increase recommendation accuracy. Moreover, we enable linked data as our data sources to collect contextual information related to events and users, and build an enhanced profile for them. As reliable resource, linked data is used to find structured knowledge and linkages among different knowledge. Finally, we conduct comprehensive experiments on various datasets in both academic community and popular social networking