000199806 001__ 199806
000199806 005__ 20180317093333.0
000199806 0247_ $$2doi$$a10.5075/epfl-thesis-6233
000199806 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6233-5
000199806 02471 $$2nebis$$a10167698
000199806 037__ $$aTHESIS_LIB
000199806 041__ $$aeng
000199806 088__ $$a6233
000199806 245__ $$aAggregating information from the crowd$$bratings, recommendations and predictions
000199806 269__ $$a2014
000199806 260__ $$aLausanne$$bEPFL$$c2014
000199806 336__ $$aTheses
000199806 502__ $$aProf. M. Finger (président) ; Prof. B. Faltings (directeur) ; Prof. A. Bernstein,  Prof. C. Tucci,  Prof. L. Xia (rapporteurs)
000199806 520__ $$aWith an ever-growing amount of data generated on the web, aggregating information from the crowd into meaningful knowledge has become crucial to companies in order to create a competitive edge. Not only companies, but also organizations and governments can benefit from this aggregation. In this thesis, we illustrate the benefits of aggregating information from the crowd with three applications. In the first application, we investigate different ways of aggregating users’ ratings on review websites. With the help of gamification techniques, we introduce a new methodology for studying users’ rating behaviour, and show that there is a balance between the amount of private information elicited from the crowd and the accuracy of the aggregated rating. In the second application, we consider the aggregation of implicit feedbacks from the crowd in order to generate personalized recommendations of news articles. We propose a new class of recommender systems based on Context Trees (CT), specifically designed for a dynamic domain like the news. We show that CT recommender systems generate accurate and novel recommendations in an offline setting, but also in real time on the newspaper website swissinfo.ch. In the last application, we address the problem of eliciting private information to predict outcomes of events. We report on an experimental platform called swissnoise that allows users to express their opinions on various topics ranging from sports to politics. It is the first platformto implement a peer prediction mechanism for online opinion polls. We show that peer prediction can be practically implemented, and discuss the design choices and variations made to such mechanism. Finally, we find that peer prediction achieves a performance comparable to that of prediction markets
000199806 6531_ $$aaggregation
000199806 6531_ $$acrowd
000199806 6531_ $$aratings
000199806 6531_ $$arecommendation
000199806 6531_ $$anews
000199806 6531_ $$aopinion poll
000199806 6531_ $$aprediction market
000199806 6531_ $$apeer prediction
000199806 700__ $$0241989$$aGarcin, Florent Frédéric$$g154647
000199806 720_2 $$0240959$$aFaltings, Boi$$edir.$$g105074
000199806 8564_ $$s4756979$$uhttps://infoscience.epfl.ch/record/199806/files/EPFL_TH6233.pdf$$yn/a$$zn/a
000199806 909CO $$ooai:infoscience.tind.io:199806$$pIC$$pthesis$$pthesis-bn2018
000199806 909C0 $$0252184$$pLIA$$xU10406
000199806 917Z8 $$x108898
000199806 917Z8 $$x108898
000199806 918__ $$aIC$$cIIF$$dEDMT
000199806 919__ $$aLIA
000199806 920__ $$a2014-6-27$$b2014
000199806 970__ $$a6233/THESES
000199806 973__ $$aEPFL$$sPUBLISHED
000199806 980__ $$aTHESIS