Investigating the impact of recommender systems on user-based and item-based popularity bias. Elahi, M., Kholgh, D. K., Kiarostami, M. S., Saghari, S., Rad, S. P., & Tkalčič, M. Information Processing & Management, 58(5):102655, September, 2021.
Investigating the impact of recommender systems on user-based and item-based popularity bias [link]Paper  doi  abstract   bibtex   
Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.
@article{elahi_investigating_2021,
	title = {Investigating the impact of recommender systems on user-based and item-based popularity bias},
	volume = {58},
	copyright = {All rights reserved},
	issn = {03064573},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0306457321001436},
	doi = {10.1016/j.ipm.2021.102655},
	abstract = {Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.},
	language = {en},
	number = {5},
	urldate = {2022-10-04},
	journal = {Information Processing \& Management},
	author = {Elahi, Mehdi and Kholgh, Danial Khosh and Kiarostami, Mohammad Sina and Saghari, Sorush and Rad, Shiva Parsa and Tkalčič, Marko},
	month = sep,
	year = {2021},
	pages = {102655},
}

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