Abstract
A major shortcoming of content-based approaches exists in the representation of the user model. Content-based approaches often employ term vectors to represent each user's interest. In doing so, they ignore the semantic relations between terms of the vector space model in which indexed terms are not orthogonal and often have semantic relatedness between one another.
In this paper, we improve the representation of a user model during building user model in content-based approaches by performing these steps. First is the domain concept filtering in which concepts and items of interests are compared to the domain ontology to check the relevant items to our domain using ontology based semantic similarity. Second, is incorporating semantic content into the term vectors. We use word definitions and relations provided by WordNet to perform word sense disambiguation and employ domain-specific concepts as category labels for the semantically enhanced user models. The implicit information pertaining to the user behavior was extracted from click stream data or web usage sessions captured within the web server logs.
Also, our proposed approach aims to update user model, we should analysis user's history query keywords. For a certain keyword, we extract the words which have the semantic relationships with the keyword and add them into the user interest model as nodes according to semantic relationships in the WordNet.