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Popularity Bias in Recommender Systems - A Review

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Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT (ICETCE 2022)

Abstract

With the advancement in recommendation techniques, focus is diverted from just making them more accurate to making them fairer and diverse, thus catering to the set of less-popular items (the long tail) that often get neglected due to inherent biases in recommender systems. Popularity bias has been recently acknowledged as a major bias of critical concern in the field of recommender systems. Although research on popularity bias has gained pace from the last couple of years, this field is believed to be still in its infancy. To advance research in this area, this paper thoroughly investigates current state of the art and could have a very positive impact on further research in popularity bias. Besides the mitigation techniques discussed in this paper, allied evaluation metrics that were used in measuring popularity bias have also been discussed.

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Acknowledgement

This research is funded by IUST Kashmir, J&K under grant number IUST/Acad/RP_APP/18/99.

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Correspondence to Muzafar Rasool Bhat .

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Ahanger, A.B., Aalam, S.W., Bhat, M.R., Assad, A. (2022). Popularity Bias in Recommender Systems - A Review. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT. ICETCE 2022. Communications in Computer and Information Science, vol 1591. Springer, Cham. https://doi.org/10.1007/978-3-031-07012-9_37

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  • DOI: https://doi.org/10.1007/978-3-031-07012-9_37

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  • Online ISBN: 978-3-031-07012-9

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