@article{balchanowski_comparative_2023,
	title = {A {Comparative} {Study} of {Rank} {Aggregation} {Methods} in {Recommendation} {Systems}},
	volume = {25},
	issn = {1099-4300},
	url = {https://www.mdpi.com/1099-4300/25/1/132},
	doi = {10.3390/e25010132},
	abstract = {The aim of a recommender system is to suggest to the user certain products
or services that most likely will interest them. Within the context of
personalized recommender systems, a number of algorithms have been
suggested to generate a ranking of items tailored to individual user
preferences. However, these algorithms do not generate identical
recommendations, and for this reason it has been suggested in the
literature that the results of these algorithms can be combined using
aggregation techniques, hoping that this will translate into an
improvement in the quality of the final recommendation. In order to see
which of these techniques increase the quality of recommendations to the
greatest extent, the authors of this publication conducted experiments in
which they considered five recommendation algorithms and 20 aggregation
methods. The research was carried out on the popular and publicly
available MovieLens 100k and MovieLens 1M datasets, and the results were
confirmed by statistical tests.},
	number = {1},
	urldate = {2023-01-12},
	journal = {Entropy},
	author = {Bałchanowski, Michał and Boryczka, Urszula},
	month = jan,
	year = {2023},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	pages = {132},
}

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