A pseudo-supervised approach to improve a recommander based on collaborative filtering

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

Volumn 2702, Issue , 2003, Pages 429-431

  Martín Guerrero, José D ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; HIERARCHICAL SYSTEMS; IMAGE PROCESSING; LEARNING SYSTEMS; MAPPING; SELF ORGANIZING MAPS; VECTOR QUANTIZATION; WEBSITES;

HIERARCHICAL CLUSTERING ALGORITHMS; LEARNING VECTOR QUANTIZATION (LVQ); MODULAR SYSTEM; PSEUDO-SUPERVISED LEARNING; DYNAMIC RECOMMENDATIONS; IMPROVE-A; PHD THESIS; SUPERVISED AND UNSUPERVISED LEARNING;

WEBSITES; COLLABORATIVE FILTERING;

EID: 8344264508     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44963-9_65     Document Type: Conference Paper

References (3)

1. Perkowitz, M., Etzioni, O.: Towards adaptive Web sites: Conceptual framework and case study. Artificial Intelligence, Vol. 118. Elsevier (2000) 245-275.
2. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK (1996).
3. Theodoridis, S., Koutroumbas,K.: Pattern Recognition. Academic Press, a division of Harcourt Brace & Company, San Diego, CA, USA (1999).