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.
CITATION STYLE
Fuchs, M., & Zanker, M. (2012). Multi-criteria ratings for recommender systems: An empirical analysis in the tourism domain. In Lecture Notes in Business Information Processing (Vol. 123 LNBIP, pp. 100–111). Springer Verlag. https://doi.org/10.1007/978-3-642-32273-0_9
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