Title :
Semantic feedback for hybrid recommendations in Recommendz
Author :
Garden, Matthew ; Dudek, Gregory
Author_Institution :
Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada
fDate :
29 March-1 April 2005
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;
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
DOI :
10.1109/EEE.2005.115
