Personalized Information Recommender System Research Based On Ontology For Social Network

Data mining emerged as rapidly growing technology in order to extract valuable information and knowledge in large volumes data. One of the serious problems of these environments is information overload problem. Based on the issue that how to obtain the interesting information for users quickly and effectively from large volumes data, in this paper explain the impact of ontology and data mining techniques on improving of content-based social network. At first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and WordNet and then they will be used as an input in two kind of Recommender System (RS): one, fully ontology-based and one with enriching the user profile vector with ontology in Vector Space Model (VSM) (proposed method), obtained new content recommendation items based on user interest. The experimental results show that the simple VSM based RS can not only improve the accuracy of the content recommender, but also extend the coverage of content.