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Critiquing recommenders for public taste products
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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents
New York, New York, USA
SESSION: Short papers table of contents
Pages 249-252  
Year of Publication: 2009
ISBN:978-1-60558-435-5
Authors
Pearl Pu  EPFL, Lausanne, Switzerland
Maoan Zhou  EPFL, Lausanne, Switzerland
Sylvain Castagnos  EPFL, Lausanne, Switzerland
Sponsor
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

Critiquing-based recommenders do not require users to state all of their preferences upfront or rate a set of previously experienced products. Compared to other types of recommenders, they require relatively little user effort, especially initially, despite potential accuracy problems. On the other hand, they rely on a set of critiques to elicit users feedback in order to improve accuracy. Thus the better the critiques are, the more accurately and efficiently the system becomes in generating its recommendations. This method has been successfully applied to high-involvement products. However, it was never tested on public taste products such as music, films, perfumes, fashion goods or wine. Indeed our initial trial adapting traditional critiquing methods to this new domain led to unsatisfactory results. This has motivated us to develop a novel approach named "editorial picked critiques" (EPC) that accounts for users' needs for popularity information, editorial suggestions, as well as their needs for personalization and diversity. Through an empirical study, we demonstrate that EPC presents a viable recommender approach and is superior on several dimensions to critiques generated by data mining methods.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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