Authors:
Yinuo Zhang
;
Hao Wu
;
Vikram Sorathia
and
Viktor K. Prasanna
Affiliation:
University of Southern California, United States
Keyword(s):
Recommendation, Linked Data, Social Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Cloud Computing
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web Technologies
;
Services Science
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
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 service.
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