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. Keywords-User model; Domain ontology; Semantic Similarity; Wordnet.
Highlights
User model [1] is a collection of personal information
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 items relevance is based on ontology-based semantic similarity where browsed items by a learner on the web are compared to the items from a domain ontology and learner profile
Summary
User model [1] is a collection of personal information. The information is stored without adding further description or interpreting this information. User model represents cognitive skills, intellectual abilities, intentions, learning styles, preferences and interactions with the system These properties are stored after assigning them values. User Modeling is an active research area in e-learning and personalization, especially when abstracting the user away from the problem an abstraction that has, over the years, contributed to the design of more effective e-learning systems. Despite this improvement, the main focus in most systems, for the past decade, has been on models that are ”good for all users”, and not for a specific user. The method of updating user model was proposed in [5, 6]
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