Taxonomy-driven computation of product recommendations Export

@inproceedings{citeulike:2364697, abstract = {Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years. At the same time, very large taxonomies for product classification are becoming increasingly prominent among e-commerce systems for diverse domains, rendering detailed machine-readable content descriptions feasible. Amazon.com makes use of an entire plethora of hand-crafted taxonomies classifying books, movies, apparel, and various other goods. We exploit such taxonomic background knowledge for the computation of personalized recommendations. Hereby, relationships between super-concepts and sub-concepts constitute an important cornerstone of our novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen. Ample empirical analysis, both offline and online, demonstrates our proposal's superiority over common existing approaches when user information is sparse and implicit ratings prevail.}, address = {New York, NY, USA}, author = {Ziegler, Cai N. and Lausen, Georg and Thieme, Lars S.}, booktitle = {CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management}, citeulike-article-id = {2364697}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1031171.1031252}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1031171.1031252}, doi = {10.1145/1031171.1031252}, isbn = {1-58113-874-1}, keywords = {ontology, recommender, user-profile}, location = {Washington, D.C., USA}, pages = {406--415}, posted-at = {2009-07-28 17:09:32}, priority = {2}, publisher = {ACM}, title = {Taxonomy-driven computation of product recommendations}, url = {http://dx.doi.org/10.1145/1031171.1031252}, year = {2004} }

TY - CONF ID - citeulike:2364697 L3 - citeulike-article-id:2364697 N2 - Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years. At the same time, very large taxonomies for product classification are becoming increasingly prominent among e-commerce systems for diverse domains, rendering detailed machine-readable content descriptions feasible. Amazon.com makes use of an entire plethora of hand-crafted taxonomies classifying books, movies, apparel, and various other goods. We exploit such taxonomic background knowledge for the computation of personalized recommendations. Hereby, relationships between super-concepts and sub-concepts constitute an important cornerstone of our novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen. Ample empirical analysis, both offline and online, demonstrates our proposal's superiority over common existing approaches when user information is sparse and implicit ratings prevail. CY - Washington, D.C., USA SN - 1-58113-874-1 AD - New York, NY, USA EP - 415 TI - Taxonomy-driven computation of product recommendations PB - ACM T2 - CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management SP - 406 KW - ontology KW - recommender KW - user-profile AU - Ziegler, Cai AU - Lausen, Georg AU - Thieme, Lars PY - 2004/// UR - http://dx.doi.org/10.1145/1031171.1031252 ER -