Analogy in recommendation. Numerical vs. Ordinal: A discussion

Hug, N; Prade, H; Richard, G; Serrurier, M

Analogy in recommendation. Numerical vs. Ordinal: A discussion

Hug, N Prade, H

Richard, G Serrurier, M

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 2220 - 2226
Issue Date:: 2016-11-07

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Field	Value	Language
dc.contributor.author	Hug, N	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Richard, G	en_US
dc.contributor.author	Serrurier, M	en_US
dc.date.issued	2016-11-07	en_US
dc.identifier.citation	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 2220 - 2226	en_US
dc.identifier.isbn	9781509006250	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119822
dc.description.abstract	© 2016 IEEE. The paper investigates the use of analogical reasoning for recommendation purposes. More particularly, we address the problem of predicting missing ratings on the basis of known ones. After discussing the differences with another recently experimented approach based on analogical proportions, a new analogical approach is proposed. It relies on the intuition that "the rating of user u for item i is to the rating of user v for item i as the rating of user u for item j is to the rating of user v for item j". This leads to algorithms yielding results close to the ones of state-of-The art approaches, when the ratings are regarded as numerical quantities. This is due to the fact that these latter approaches embed an estimation process that is implicitly close to analogy, as discussed in this paper. An analogical approach is also outlined and briefly discussed when the ratings are supposed to have an ordinal meaning only.	en_US
dc.relation.ispartof	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2016.7737969	en_US
dc.title	Analogy in recommendation. Numerical vs. Ordinal: A discussion	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. The paper investigates the use of analogical reasoning for recommendation purposes. More particularly, we address the problem of predicting missing ratings on the basis of known ones. After discussing the differences with another recently experimented approach based on analogical proportions, a new analogical approach is proposed. It relies on the intuition that "the rating of user u for item i is to the rating of user v for item i as the rating of user u for item j is to the rating of user v for item j". This leads to algorithms yielding results close to the ones of state-of-The art approaches, when the ratings are regarded as numerical quantities. This is due to the fact that these latter approaches embed an estimation process that is implicitly close to analogy, as discussed in this paper. An analogical approach is also outlined and briefly discussed when the ratings are supposed to have an ordinal meaning only.

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http://hdl.handle.net/10453/119822