Web-Page Recommendation in Data Retrieval Exploitation Domain Information and Web Usage Mining

Web-page recommendation plays an important role in intelligent internet systems. Helpful knowledge discovery from internet usage data and satisfactory knowledge illustration for effective Web-page recommendations unit crucial and challenging. This paper proposes a singular technique to expeditiously give higher Web-page recommendation through semantic-enhancement by human action the domain and Web usage knowledge of an online website. Two new models are proposed to represent the domain knowledge. The primary model uses ontology to represent the domain information. The second model uses one automatically generated linguistics network to represent domain terms, Web-pages, and the relations between them. Another new model, the abstract prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the mixing of domain knowledge and internet usage knowledge. A number of effective queries are developed to questing relating to these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results are compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method.