A Domain Ontology in Social Networks for Identifying User Interest for Personalized Recommendations

Social media and the development of web 2.0 encourage the user to participate more interactively in social networks. In social network relationships may be identified by the user posts and interactions. Using this data, the system can make recommendations tailored to specific users. However, when the user is on social network for the first time, the recommendation system cannot make recommendations, since the user has no history. In this paper, we design an ontology combined with social networks. We develop the ontology based on data from users and their friends. Using the user interest and community influences, we propose a system to solve the cold start problem in recommendation systems. The system calculates the similarity between users, using user preferences and uses a rule generating algorithm to create the dynamic inference rule. The ontology is updated each time the content of the personal ontology is updated. The newest ontology will be retained to increase the accuracy the next time the recommendation system is executed.

[1]  Siu Cheung Hui,et al.  Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis , 2012, IEEE Transactions on Affective Computing.

[2]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[3]  Ohbyung Kwon,et al.  A Galois lattice approach to a context-aware privacy negotiation service , 2011, Expert Syst. Appl..

[4]  Jano Moreira de Souza,et al.  Building a Personal Knowledge Recommendation System using Agents, Learning Ontologies and Web Mining , 2006, 2006 10th International Conference on Computer Supported Cooperative Work in Design.

[5]  Fei Wang,et al.  Social contextual recommendation , 2012, CIKM.

[6]  N. Latha,et al.  Personalized Recommendation Combining User Interest and Social Circle , 2015 .

[7]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[8]  Xueming Qian,et al.  Tagging photos using users' vocabularies , 2013, Neurocomputing.

[9]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic Web , 2006, IEEE Transactions on Knowledge and Data Engineering.

[10]  Harold Boley.  The RuleML Family of Web Rule Languages , 2006, PPSWR.

[11]  Harold Boley,et al.  Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses, and Order-Sorted Terms , 2003, RuleML.

[12]  Ernest Friedman-Hill,et al.  Jess in action : rule-based systems in Java , 2003 .

[13]  Lei Tang,et al.  Large-scale behavioral targeting with a social twist , 2011, CIKM '11.

[14]  Dongman Lee,et al.  Inferring User Interest Using Familiarity and Topic Similarity with Social Neighbors in Facebook , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[15]  Mariam Rady.  Generating an Excerpt of a Service Level Agreement from a Formal Definition of Non-Functional Aspects Using OWL , 2014, J. Univers. Comput. Sci..