Looking for “Good” Recommendations: A Comparative Evaluation of Recommender Systems

  • Paolo Cremonesi
  • Franca Garzotto
  • Sara Negro
  • Alessandro Vittorio Papadopoulos
  • Roberto Turrin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6948)

Abstract

A number of researches in the Recommender Systems (RSs) domain suggest that the recommendations that are “best” according to objective metrics are sometimes not the ones that are most satisfactory or useful to the users. The paper investigates the quality of RSs from a user-centric perspective. We discuss an empirical study that involved 210 users and considered seven RSs on the same dataset that use different baseline and state-of-the-art recommendation algorithms. We measured the user’s perceived quality of each of them, focusing on accuracy and novelty of recommended items, and on overall users’ satisfaction. We ranked the considered recommenders with respect to these attributes, and compared these results against measures of statistical quality of the considered algorithms as they have been assessed by past studies in the field using information retrieval and machine learning algorithms.

Keywords

Recommender systems quality metrics user study 

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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Paolo Cremonesi
    • 1
  • Franca Garzotto
    • 1
  • Sara Negro
    • 1
  • Alessandro Vittorio Papadopoulos
    • 1
  • Roberto Turrin
    • 2
  1. 1.Department of Electronics and InformationPolitecnico di MilanoMilanoItaly
  2. 2.Moviri srl, R&DItaly

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