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]](https://bibbase.org/img/filetypes/link.svg "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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