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Re-using Implicit Knowledge in Short-term Information Profiles for Context-sensitive Tasks |
Hayes, Conor; Cunningham, Padraig
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TCD-CS-2005-28 Typically, case-based recommender systems recommend single items
to the on-line customer. In this paper we introduce the idea of recommending a
user-defined collection of items where the user has implicitly encoded the
relationships between the items. Automated collaborative filtering (ACF), a so-called
`contentless? technique, has been widely used as a recommendation
strategy for music items. However, its reliance on a global model of the user?s
interests makes it unsuited to catering for the user?s local interests. We consider
the context-sensitive task of building a compilation, a user-defined collection of
music tracks. In our analysis, a collection is a case that captures a specific shortterm
information/music need. In an offline evaluation, we demonstrate how a
case-completion strategy that uses short-term representations is significantly
more effective than the ACF technique. We then consider the problem of
recommending a compilation according to the user?s most recent listening
preferences. Using a novel on-line evaluation where two algorithms compete
for the user?s attention, we demonstrate how a knowledge-light case-based
reasoning strategy successfully addresses this problem.
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Keyword(s):
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Computer Science |
Publication Date:
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2005 |
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Type:
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Report |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Hayes, Conor; Cunningham, Padraig. 'Re-using Implicit Knowledge in Short-term Information Profiles for Context-sensitive Tasks'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-28, 2005, pp15 |
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Publisher(s):
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Trinity College Dublin, Department of Computer Science |
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File Format(s):
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application/pdf |
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First Indexed:
2014-05-13 05:31:07 Last Updated:
2015-04-10 05:13:50 |
