Ontological user profiling in recommender systems.

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, repr...

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Main Authors: Middleton, SE, Shadbolt, N, Roure, D
Format: Journal article
Language:English
Published: 2004
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author Middleton, SE
Shadbolt, N
Roure, D
author_facet Middleton, SE
Shadbolt, N
Roure, D
author_sort Middleton, SE
collection OXFORD
description We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
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spelling oxford-uuid:740d6b2d-0483-450c-93e2-043a937d5a282022-03-26T20:00:14ZOntological user profiling in recommender systems.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:740d6b2d-0483-450c-93e2-043a937d5a28EnglishSymplectic Elements at Oxford2004Middleton, SEShadbolt, NRoure, DWe explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
spellingShingle Middleton, SE
Shadbolt, N
Roure, D
Ontological user profiling in recommender systems.
title Ontological user profiling in recommender systems.
title_full Ontological user profiling in recommender systems.
title_fullStr Ontological user profiling in recommender systems.
title_full_unstemmed Ontological user profiling in recommender systems.
title_short Ontological user profiling in recommender systems.
title_sort ontological user profiling in recommender systems
work_keys_str_mv AT middletonse ontologicaluserprofilinginrecommendersystems
AT shadboltn ontologicaluserprofilinginrecommendersystems
AT roured ontologicaluserprofilinginrecommendersystems