Capturing knowledge of user preferences: ontologies in recommender systems Export

@inproceedings{citeulike:213200, abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.}, address = {New York, NY, USA}, author = {Middleton, Stuart E. and De Roure, David C. and Shadbolt, Nigel R.}, booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture}, citeulike-article-id = {213200}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=500755}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/500737.500755}, doi = {10.1145/500737.500755}, isbn = {1-58113-380-4}, keywords = {capturing, knowledge, learning, mining, ontologies, preferences, recommender, systems, user, usermodel, userprofile}, location = {Victoria, British Columbia, Canada}, pages = {100--107}, posted-at = {2009-10-28 13:32:20}, priority = {2}, publisher = {ACM}, title = {Capturing knowledge of user preferences: ontologies in recommender systems}, url = {http://dx.doi.org/10.1145/500737.500755}, year = {2001} }

TY - CONF ID - citeulike:213200 L3 - citeulike-article-id:213200 N2 - Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list. CY - Victoria, British Columbia, Canada SN - 1-58113-380-4 AD - New York, NY, USA EP - 107 TI - Capturing knowledge of user preferences: ontologies in recommender systems PB - ACM T2 - K-CAP '01: Proceedings of the 1st international conference on Knowledge capture SP - 100 KW - capturing KW - knowledge KW - learning KW - mining KW - ontologies KW - preferences KW - recommender KW - systems KW - user KW - usermodel KW - userprofile AU - Middleton, Stuart AU - De Roure, David AU - Shadbolt, Nigel PY - 2001/// UR - http://dx.doi.org/10.1145/500737.500755 ER -