@inproceedings{carvalho_fair_2018,
	title = {{FAiR}: {A} {Framework} for {Analyses} and {Evaluations} on {Recommender} {Systems}},
	url = {http://dx.doi.org/10.1007/978-3-319-95168-3_26},
	doi = {10.1007/978-3-319-95168-3_26},
	abstract = {Recommender systems (RSs) have become essential tools in e-commerce
applications, helping users in the decision-making process. Evaluation on
these tools is, however, a major divergence point nowadays, since there is
no consensus regarding which metrics are necessary to consolidate new RSs.
For this reason, distinct frameworks have been developed to ease the
deployment of RSs in research and/or production environments. In the
present work, we perform an extensive study of the most popular evaluation
metrics, organizing them into three groups: Effectiveness-based,
Complementary Dimensions of Quality and Domain Profiling. Further, we
consolidate a framework named FAiR to help researchers in evaluating their
RSs using these metrics, besides identifying the characteristics of data
collections that may intrinsically affect RSs performance. FAiR is
compatible with the output format of the main existing RSs libraries
(i.e., MyMediaLite and LensKit).},
	publisher = {Springer International Publishing},
	author = {Carvalho, Diego and Silva, Nícollas and Silveira, Thiago and Mourão, Fernando and Pereira, Adriano and Dias, Diego and Rocha, Leonardo},
	year = {2018},
	pages = {383--397},
}

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