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Semantic Feedback for Hybrid Recommendations in Recommendz
Hong Kong, China March 29-April 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/EEE.2005.1152005 IEEE International Conference on ...
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Matthew Garden, Gregory Dudek, "Semantic Feedback for Hybrid Recommendations in Recommendz," e-Technology, e-Commerce, and e-Services, IEEE International Conference on, pp. 754-759, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05), 2005.
 
 
BibTex
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@article{ 10.1109/EEE.2005.115,
author = {Matthew Garden and Gregory Dudek},
title = {Semantic Feedback for Hybrid Recommendations in Recommendz},
journal ={e-Technology, e-Commerce, and e-Services, IEEE International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2274-2},
pages = {754-759},
doi = {http://doi.ieeecomputersociety.org/10.1109/EEE.2005.115},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - e-Technology, e-Commerce, and e-Services, IEEE International Conference on
TI - Semantic Feedback for Hybrid Recommendations in Recommendz
SN - 0-7695-2274-2
SP754
EP759
A1 - Matthew Garden,
A1 - Gregory Dudek,
PY - 2005
KW - null
VL - 0
JA - e-Technology, e-Commerce, and e-Services, IEEE International Conference on
ER -
 
Matthew Garden, McGill University, Canada
Gregory Dudek, McGill University, Canada
In this paper we discuss the Recommendz recommender system. This domain-independent system combines the advantages of collaborative and content-based filtering in a novel way. By allowing users to provide feedback not only about an item as a whole, but also properties of an item that motivated their opinion, increased performance seems to be achieved. The features used to describe items are specified by the users of the system rather than predetermined using manual knowledge-engineering. We describe a method for combining descriptive features and simple ratings, and provide a performance analysis.
Citation:
Matthew Garden, Gregory Dudek, "Semantic Feedback for Hybrid Recommendations in Recommendz," eee, pp.754-759, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05), 2005
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