TY - GEN
T1 - Reproducibility of experiments in recommender systems evaluation
AU - Polatidis, Nikolaos
AU - Kapetanakis, Stylianos
AU - Pimenidis, Elias
AU - Kosmidis, Konstantinos
N1 - This is a post-peer-review, pre-copyedit version of an article published in IFIP Advances in Information and Communication Technology. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-92007-8_34
PY - 2018/5/22
Y1 - 2018/5/22
N2 - Recommender systems evaluation is usually based on predictiveaccuracy metrics with better scores meaning recommendations of higherquality. However, the comparison of results is becoming increasingly difficult,since there are different recommendation frameworks and different settings inthe design and implementation of the experiments. Furthermore, there might beminor differences on algorithm implementation among the differentframeworks. In this paper, we compare well known recommendationalgorithms, using the same dataset, metrics and overall settings, the results ofwhich point to result differences across frameworks with the exact samesettings. Hence, we propose the use of standards that should be followed asguidelines to ensure the replication of experiments and the reproducibility ofthe results.
AB - Recommender systems evaluation is usually based on predictiveaccuracy metrics with better scores meaning recommendations of higherquality. However, the comparison of results is becoming increasingly difficult,since there are different recommendation frameworks and different settings inthe design and implementation of the experiments. Furthermore, there might beminor differences on algorithm implementation among the differentframeworks. In this paper, we compare well known recommendationalgorithms, using the same dataset, metrics and overall settings, the results ofwhich point to result differences across frameworks with the exact samesettings. Hence, we propose the use of standards that should be followed asguidelines to ensure the replication of experiments and the reproducibility ofthe results.
KW - Recommender systems
KW - Evaluation
KW - Reproducibility
KW - Replication
M3 - Conference contribution with ISSN or ISBN
VL - 519
T3 - IFIP Advances in Information and Communication Technology
SP - 401
EP - 409
BT - 14th International Conference on Artificial Intelligence Applications and Innovations
PB - Springer-Verlag
CY - Germany
T2 - 14th International Conference on Artificial Intelligence Applications and Innovations
Y2 - 22 May 2018
ER -
