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.
In REF , Cremonesi et al. report on an empirical study involving 210 users which explored the users perceived quality of seven different RSs on the same dataset in an offline evaluation.
Looking for good recommendations: a comparative evaluation of recommender systems
INTERACT
Computer Science