Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: An Empirical Study PAOLO CREMONESI and FRANCA GARZOTTO, Politecnico di Milano ROBERTO TURRIN, Moviri Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users trust towards in an application and orient their decisions or actions towards speci c directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, de ned by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, de ned by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Net ix), but each with a different recommender algorithm. In
ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery
Published: Jun 1, 2012
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