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Recent studies have shown that recommendation systems commonly suffer from popularity bias.
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Gopalan, P., Hofman, J.M., Blei, D.M.: Scalable recommendation with hierarchical poisson factorization. In: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. pp. 326–335 (2015)
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He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. pp. 173–182. ACM, Perth, Australia (2017)
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Alharthi, H., Inkpen, D., Szpakowicz, S.: A survey of book recommender systems. Journal of Intelligent Information Systems 51
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Ciampaglia, G.L., Nematzadeh, A., Menczer, F., Flammini, A.: How algorithmic popularity bias hinders or promotes quality. Scientific reports 8
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Kowald, D., Schedl, M., Lex, E.: The unfairness of popularity bias in music recommendation: a reproducibility study. Advances in Information Retrieval 12036
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Rahmani, H.A., Aliannejadi, M., Baratchi, M., Crestani, F.: Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation. In: European Conference on Information Retrieval. pp. 205–219. Springer, Lisboa, Portugal (Online) (2020)
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Salah, A., Truong, Q.T., Lauw, H.W.: Cornac: A comparative framework for multimodal recommender systems. Journal of Machine Learning Research 21
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Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B., Malthouse, E.: User-centered evaluation of popularity bias in recommender systems. In: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. pp. 119–129 (2021)
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2018
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Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking. In: The thirty-second international flairs conference (2019)
2019
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2019
Cited alongside, same era.
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR) 52
2019
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2021
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Deldjoo, Y., Bellogin, A., Di Noia, T.: Explaining recommender systems fairness and accuracy through the lens of data characteristics. Information Processing & Management 58
2021
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Truong, Q.T., Salah, A., Tran, T.B., Guo, J., Lauw, H.W.: Exploring cross-modality utilization in recommender systems. IEEE Internet Computing (2021)
2021
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Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54
2021
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