Beyond learners' interest: Personalized paper recommendation based on their pedagogical features for an e-learning system

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3157, Issue , 2004, Pages 301-310

  Tang, Tiffany ^a,b   McCalla, Gordon ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COSTS; ELECTRONIC COMMERCE; MARKETING; STUDENTS; WORLD WIDE WEB; ARTIFICIAL INTELLIGENCE; COLLABORATIVE FILTERING; COMPUTER AIDED INSTRUCTION; E-LEARNING; EDUCATION; RECOMMENDER SYSTEMS;

E-LEARNING; LEARNING PROCESS; PEDAGOGY; RECOMMENDER SYSTEMS (RS); BACK-GROUND KNOWLEDGE; HYBRID RECOMMENDATION; PAPER RECOMMENDATIONS; PEDAGOGICAL FEATURES; PERSONALIZED RECOMMENDATION; RECOMMENDATION TECHNIQUES; SIMILARITY MEASUREMENTS; WEB-BASED LEARNING SYSTEMS;

LEARNING SYSTEMS;

EID: 22944488572     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-28633-2_33     Document Type: Conference Paper

References (12)

1. Basu, C., Hirsh, H., Cohen, W. and Nevill-Manning, C. Technical paper recommendations: a study in combining multiple information sources. JAIR, 1, 231-252. 2001.
2. Bollacker, K., Lawrence, S. and C. Lee Giles, C. L. A system for automatic personalized tracking of scientific literature on the web. ACM DL, 105-113. 1999.
3. Chan, T. and Baskin, A.B. Learning companion systems. ITS 1990, 6-33. 1990.
4. Herlocker, J., Konstan, J., Borchers, A. and Riedl, J. An algorithmic framework for performing collaborative filtering. SIGIR'99, 230-237. 1999.
5. Kobsa, A., Koenemann, J. and Pohl, W. Personalized hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review 16(2): 111-155.2001.
6. McNee, S, Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S., Rashid, A., Konstan, J. and Riedl, J. On the Recommending of Citations for Research Papers. ACM CSCW'02, 116-125. 2002.
7. Schafer, J., Konstan, J. and Riedl, J. Electronic Commerce Recommender Applications. Data Mining and Knowledge Discovery, 5, (1/2): 115-152, 2001.
8. Tang, T.Y. and Chan, K. C. C. Feature construction for student group forming based on their browsing behaviors in an e-learning system. PRICAI 2002, 512-521. 2002.
9. Tang, T.Y and McCalla, G. Smart recommendations for an evolving e-learning system. In Workshop on Technologies for Electronic Documents for Supporting Learning, AIED'2003.
10. Tang, T.Y. and McCalla, G. On the pedagogically guided paper recommendation for an evolving web-based learning system. Proc. of FLAIRS Conference, 2004. AAAI Press.
11. Weber, G.,and Brusilovsky, P. ELM-ART: an adaptive versatile system for web-based instruction. Internationaljournal of AI in Education, 12: 1-35. 2001.
12. Woodruff, A., Gossweiler, R., Pitkow, J., Chi, E. and Card, S. Enhancing a digital book with a reading recommender. In ACM CHI 2000, 153-160. 2000.