Abstract
Recommender Systems are usually evaluated by one or two metrics. Due to the multifaceted nature of recommender systems, however, it is insufficient to evaluate them using only one metric. This paper presents my Ph.D. research agenda on evaluating recommenders from different points of view. In particular, I aim to provide a comprehensive evaluation framework that merges different metrics and comes up with an overall result evaluation. The proposed framework is built based on an inferred correlation between the most important metrics and a weight function that assign different weights for different metrics based on the application area of the recommender. This work can be used to evaluate different recommender types that are applied to the most popular application areas such as movies, documents, etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kowald, D., Lex, E.: Evaluating tag recommender algorithms in real-world folksonomies: a comparative study. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 265–268. ACM (2015)
Avazpour, I., Pitakrat, T., Grunske, L., Grundy, J.: Dimensions and metrics for evaluating recommendation systems. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 245–273. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_10
Said, A., et al.: Recommender systems evaluation: a 3D benchmark. In: RUE@ RecSys (2012)
Karthwohl, D.R., Anderson, W.: A revision of Bloom’s taxonomy: an overview theory into practice. The Ohio State University (2002)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods, and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Alslaity, A. (2018). A Unified Evaluation Framework for Recommenders. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-89656-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-89655-7
Online ISBN: 978-3-319-89656-4
eBook Packages: Computer ScienceComputer Science (R0)