What Is in a Like? Preference Aggregation on the Social Web

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), Furnkranz and Hullermeier (Kunstliche Intelligenz 19(1):60–61, 2005), and Hullermeier et al. (Artif. Intel. 172(16–17):1897–1916, 2008).

[2]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

[3]  Daniel Baier,et al.  Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? , 2014 .

[4]  Online Conjoint — Chancen und Grenzen: Ein Fallbeispiel aus dem Telekommunikationsmarkt , 2003 .

[5]  Daniel Baier,et al.  Optimal product positioning based on paired comparison data , 1998 .

[6]  Peter Mika,et al.  Flink: Semantic Web technology for the extraction and analysis of social networks , 2005, J. Web Semant..

[7]  Francesco Bonchi,et al.  Voting in social networks , 2009, CIKM.

[8]  Ingo Schmitt Ähnlichkeitssuche in Multimedia-Datenbanken - Retrieval, Suchalgorithmen und Anfragebehandlung , 2005 .

[9]  Stefano Battiston,et al.  A model of a trust-based recommendation system on a social network , 2006, Autonomous Agents and Multi-Agent Systems.

[10]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[11]  Yutaka Matsuo,et al.  Community gravity: measuring bidirectional effects by trust and rating on online social networks , 2009, WWW '09.

[12]  Eyke Hüllermeier,et al.  Label ranking by learning pairwise preferences , 2008, Artif. Intell..

[13]  L. A. Goodman,et al.  Social Choice and Individual Values , 1951 .

[14]  Jonathan P. Thomas,et al.  The confidence heuristic: A game-theoretic analysis , 1995 .

[15]  A. Theobald Das World Wide Web als Befragungsinstrument , 2000 .

[16]  Adrian Giurca,et al.  Adaptive conjoint analysis. Training data: Knowledge or beliefs?: A logical perspective of preferences as beliefs , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[17]  Manfred K. Warmuth,et al.  The Weighted Majority Algorithm , 1994, Inf. Comput..

[18]  Panagiotis Symeonidis,et al.  Product recommendation and rating prediction based on multi-modal social networks , 2011, RecSys '11.

[19]  P. Fishburn The Theory Of Social Choice , 1973 .

[20]  Daniel Baier,et al.  Conjoint Analysis and Stimulus Presentation — a Comparison of Alternative Methods , 2002 .

[21]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[22]  Adrian Giurca,et al.  Can Adaptive Conjoint Analysis perform in a Preference Logic Framework? , 2012, KESE@ECAI.