Aggregating information from the crowd: ratings, recommendations and predictions

With 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 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


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