Research On Web Data Mining And Applying In The Personality Recommendation Of E-Learning

The thesis designs the model of personality e-learning and the model of students' information, and raises the idea of personality recommendation system of e-learning ( PRELS ) by studying the requirement of personality service of e-leaming. The system applies Web data mining technology in the recommendation of resource and behavior to the students. It makes the students increase the performance of e-learning, makes the students improve the quality of e-learning, and makes the students satisfy their requirement.Foremost, the PRELS finds out the usage pattern of the students, and provides personality service to the students, applying the Web data mining technology with the association rule and clustering technology. The system combines Web usage mining with Web content mining and Web structure mining, and increases the coverage and accuracy of personality recommendation using the relativity of the content of Web pages. Moreover, the system combines Web data mining technology with the FCCRD algorithm and the association rule to form the personality recommendation algorithm of e-learning. The FCCRD algorithm aims at the data of the students' behaviors suiting for the character of e-learning. The FCCRD algorithm transforms the data of comparability of the students' sessions to the Euclid data to improve the effect of clustering.Finally, the PRELS realizes by XML and JAVA. The thesis studies the evaluation method of the superiority of personality recommendation system and the performance test method of recommendation algorithms. And the PRELS overcomes the limitations of providing information of subjective evaluation by user, the limitations of not dealing with large numbers of data, the limitations of the outdated information of evaluation, and the limitations of using inconveniently. Ultimately, the personality recommendation algorithm incarnates the superiority of the PRELS.