Abstract
The Social Web is dominated by rating systems such as the ones of Facebook (only “Like”), YouTube (both “Like” and “Dislike”), or the Amazon product review 5-star rating. All these systems try to answer on How should a social application pool the preferences of different agents so as to best reflect the wishes of the population as a whole? The main framework is the theory of social choice (Arrow, Social choice and individual values, Wiley, New York, 1963; Fishburn, The theory of social choice, Princeton University Press, Princeton, 1973) i.e., agents have preferences, and do not try to camouflage them in order to manipulate the outcome to their personal advantage (moreover, manipulation is quite difficult when interactions take place at the Web scale). Our approach uses a combination between the Like/Dislike system and a 5-star satisfaction system to achieve local preference ranks and a global partial ranking on the outcomes set. Moreover, the actual data collection can support other preference learning techniques such as the ones introduced by Baier and Gaul (J. Econ. 89:365–392, 1999), Cohen et al. (J. Artif. Intel. Res. 10:213–270, 1999), Fürnkranz and Hüllermeier (Künstliche Intelligenz 19(1):60–61, 2005), and Hüllermeier et al. (Artif. Intel. 172(16–17):1897–1916, 2008).
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Notes
- 1.
The typical use case of groups of agents that are not partitions is when users visit different sites containing presentation units part of the same survey.
- 2.
There are many developed ranking functions. This work does not intend to compare all these various solutions.
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Giurca, A., Baier, D., Schmitt, I. (2015). What Is in a Like? Preference Aggregation on the Social Web. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_38
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