DocumentCode
2711499
Title
Semantic feedback for hybrid recommendations in Recommendz
Author
Garden, Matthew ; Dudek, Gregory
Author_Institution
Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada
fYear
2005
fDate
29 March-1 April 2005
Firstpage
754
Lastpage
759
Abstract
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.
Keywords
Internet; content-based retrieval; information filtering; knowledge engineering; Recommendz recommender system; collaborative filtering; content-based filtering; knowledge-engineering; semantic feedback; Collaboration; Databases; Feedback; Information analysis; Information filtering; Information filters; Matched filters; Motion pictures; Performance analysis; Recommender systems;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on
Print_ISBN
0-7695-2274-2
Type
conf
DOI
10.1109/EEE.2005.115
Filename
1402391
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