Personalized Course Navigation Based on Grey Relational Analysis

Abstract

Web personalization has quickly changed from a value-added facility to a service required in presenting large quantities of information because individual users of the Internet have various needs and preferences in seeking information. This paper presents a novel personalized recommendation system with online preference analysis in a distance learning environment called Coursebot. Users can both browse and search for course materials by using the interface of Coursebot. Moreover, the proposed system includes appropriate course materials ranked according to a user's interests. In this work, an analysis measure is proposed to combine typical grey relational analysis and implicit rating, and thus a user's interests are calculated from the content of documents and the user's browsing behavior. This algorithm's low computational complexity and ease of adding knowledge support online personalized analysis. In addition, the user profiles are dynamically revised to provide efficiently personalized information that reflects a user's interests after each page is visited.

A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY)

“…Collaborative personalization provides recommendations on the basis of the purchasing preferences of other customers with similar interests or demographics. It is the most successful personalization technology to date [18][19][20][21].…”