Name
Abstract | ||
|---|---|---|
Previous Bayesian classification has a problem because of reflecting semantic relation accurately in expressing characteristic of web pages. To resolve this problem, this paper proposes automatic preference mining through learning user profile with extracted information. Apriori algorithm extracts characteristic of web pages in form of association words that reflects semantic relation and it mines association words from learning the ontological user profile. Our prototype personalized movie recommender system, WebBot, extracts information about movies from web pages to recommend titles based on training movie set supplied by an individual user. The proposed method was tested in database that users estimated the preference about web pages, and certified that was more efficient than existent methods. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-27868-9_89 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
bayesian classification,recommender system,web pages | Recommender system,Information system,Data mining,Ontology,User profile,Web page,Naive Bayes classifier,Information retrieval,Computer science,Apriori algorithm,Distributed computing,The Internet | Conference |
Volume | ISSN | Citations |
3138 | 0302-9743 | 3 |
PageRank | References | Authors |
0.37 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyung-Yong Jung | 1 | 63 | 7.86 |
| Kee-Wook Rim | 2 | 154 | 24.20 |
| Jung-Hyun Lee | 3 | 188 | 23.59 |