Abstract
To provide personalized support in on-line course resources system, a semantic web-based personalized learning service is proposed to enhance the learner's learning efficiency. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. Moreover, the structural properties of the web site are often disregarded. In this Paper, we present a personalize Web search system, which can helps users to get the relevant web pages based on their selection from the domain list. In the first part of our work we present Semantic Web Personalization, a personalization system that integrates usage data with content semantics, expressed in ontology terms, in order to compute semantically enhanced navigational patterns and effectively generate useful recommendations. To the best of our knowledge, our proposed technique is the only semantic web personalization system that may be used by non-semantic web sites. In the second part of our work, we present a novel approach for enhancing the quality of recommendations based on the underlying structure of a web site. We introduce UPR (Usage-based Page Rank), a Page Rank-style algorithm that relies on the recorded usage data and link analysis techniques based on user interested domains and user query.
Highlights
Comparing with the traditional face-to-face learning style, e-learning is a revolutionary way to provide education in the life-long term
In the first part of our work we present the semantic web personalization system for Semantic Web Personalization that integrates usage data with content semantics in order to compute semantically enhanced navigational patterns and effectively generate useful recommendations
The web log data preprocessing is an essential phase in the web usage mining and personalization process
Summary
Comparing with the traditional face-to-face learning style, e-learning is a revolutionary way to provide education in the life-long term. The main data source in the web usage mining and personalization process is the information residing on the web site’s logs. Prior to processing the usage data using web mining or personalization algorithms, the information residing in the web logs should be preprocessed. Caching is heavily dependent on the client-side technologies used and cannot be dealt with In such cases, cached pages can usually be inferred using the referring information from the logs and certain heuristics, in order to re-construct the user paths, filling out the missing pages. The page view identification process involves the determination of the distinct log file accesses that contribute to a single page view. If no other means of session identification, such as cookies or session ids is used, session identification is performed using time heuristics, such as setting a minimum timeout and assumes that consecutive accesses within it belong to the same session, or a maximum timeout, assuming that two consecutive accesses that exceed it belong to different sessions [1] and [5] and [6]
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