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Multi-criteria ratings for recommender systems: An empirical analysis in the tourism domain
Mid Sweden University, Faculty of Human Sciences, Department of Social Sciences. (ETOUR)ORCID iD: 0000-0003-3964-2716
Alpen-Adria-Universität Klagenfurt, 9020 Klagenfurt, Austria.
2012 (English)In: Lecture Notes in Business Information Processing, Springer, 2012, Vol. 123 LNBIP, 100-111 p.Conference paper, Published paper (Other academic)
Abstract [en]

Most recommendation systems require some form of user feedback such as ratings in order to make personalized propositions of items. Typically ratings are unidimensional in the sense of consisting of a scalar value that represents the user's appreciation for the rated item. Multi-criteria ratings allow users to express more differentiated opinions by allowing separate ratings for different aspects or dimensions of an item. Recent approaches of multi-criteria recommender systems are able to exploit this multifaceted user feedback and make personalized propositions that are more accurate than recommendations based on unidimensional rating data. However, most proposed multi-criteria recommendation algorithms simply exploit the fact that a richer feature space allows building more accurate predictive models without considering the semantics and available domain expertise. This paper contributes on the latter aspects by analyzing multi-criteria ratings from the major etourism platform, TripAdvisor, and structuring raters' overall satisfaction with the help of a Penalty-Reward Contrast analysis. We identify that several a-priori user segments significantly differ in the way overall satisfaction can be explained by multi-criteria rating dimensions. This finding has implications for practical algorithm development that needs to consider different user segments. © 2012 Springer-Verlag.

Place, publisher, year, edition, pages
Springer, 2012. Vol. 123 LNBIP, 100-111 p.
Keyword [en]
Algorithm development; Domain expertise; Empirical analysis; eTourism; Feature space; Multi-criteria; Predictive models; Recommendation algorithms; Scalar values; User feedback; User segment
National Category
Business Administration
Identifiers
URN: urn:nbn:se:miun:diva-17260DOI: 10.1007/978-3-642-32273-0_9ISI: 000345068100009Scopus ID: 2-s2.0-84866052097Local ID: ETOURISBN: 978-364232272-3 (print)OAI: oai:DiVA.org:miun-17260DiVA: diva2:563166
Conference
13th International Conference on Electronic -Commerce and Web Technologies, EC-Web 2012;Vienna;4 September 2012through5 September 2012
Note

Source: Scopus

Available from: 2012-10-29 Created: 2012-10-27 Last updated: 2016-11-21Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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Output format
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