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Automated Case Generation for Recommender Systems Using Knowledge Discovery Techniques |
Clerkin, Patrick; Hayes, Conor; Cunningham, Padraig
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TCD-CS-2002-24 One approach to product recommendation in ecommerce
is collaborative filtering, which is based on
data of users? consumption of assets. The alternative
case-based approach is based on a more semantically
rich representation of users and assets. However, generating
these case representations can be a significant
overhead in system development. In this paper we present
an approach to case authoring based on data mining
methods. Specifically, we focus on clustering algorithms.
Having demonstrated the feasibility of this
approach, we go on to consider what benefits such
techniques might confer on the recommendation system.
In this context we distinguish three levels of interpretability
of cluster formations or concepts, and go
on to argue that, while the first two levels offer no
immediate advantages over each other in the recommendation
domain, moving to the third level allows us
to overcome the bootstrap problem of recommending
assets to new users.
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Keyword(s):
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Computer Science |
Publication Date:
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2002 |
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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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Clerkin, Patrick; Hayes, Conor; Cunningham, Padraig. 'Automated Case Generation for Recommender Systems Using Knowledge Discovery Techniques'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2002-24, 2002, pp7 |
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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 05:31:19 Last Updated:
2015-04-10 05:14:01 |
