2002/03/08 by S. E. Middleton, Stuart E. Middleton, D. C. De Roure +5
Computer Science · #Data Management and Algorithms #FOS: Computer and information sciences #I.2.11 #I.2.6 #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Recommender Systems and Techniques #Semantic Web and Ontologies #cs.LG #cs.MA
paper · pdf · doi:10.48550/arxiv.cs/0203011
First international conference on Knowledge Capture 2001, 8 pages
arxiv created 2002/03/08 · openalex publication_date 2002/03/08 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.