TY - JOUR
T1 - User-centric Evaluation of Recommender Systems in Social Learning Platforms
T2 - Accuracy is Just the Tip of the Iceberg
AU - Fazeli, Soude
AU - Drachsler, Hendrik
AU - Bitter-Rijpkema, Marlies
AU - Brouns, Francis
AU - van der Vegt, Wim
AU - Sloep, Peter B.
PY - 2018
Y1 - 2018
N2 - Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity.
AB - Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity.
KW - Recommender Systems
KW - evaluation
KW - social
KW - learning
KW - accuarcy
KW - performance
UR - http://www.scopus.com/inward/record.url?scp=85028995438&partnerID=8YFLogxK
U2 - 10.1109/TLT.2017.2732349
DO - 10.1109/TLT.2017.2732349
M3 - Article
AN - SCOPUS:85028995438
SN - 1939-1382
VL - 11
SP - 294
EP - 306
JO - IEEE Transactions on Learning Technologies
JF - IEEE Transactions on Learning Technologies
IS - 3
ER -