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Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers.
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Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference. pp. 689–698 (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)
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Dacrema, M.F., Cremonesi, P., Jannach, D.: Are we really making much progress? a worrying analysis of recent neural recommendation approaches. In: Proceedings of the 13th ACM Conference on Recommender Systems. pp. 101–109 (2019)
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Weydemann, L., Sacharidis, D., Werthner, H.: Defining and measuring fairness in location recommendations. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based recommendations, geosocial networks and geoadvertising. pp. 1–8 (2019)
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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
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Deldjoo, Y., Di Noia, T., Di Sciascio, E., Merra, F.A.: How dataset characteristics affect the robustness of collaborative recommendation models. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 951–960 (2020)
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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., Ahmadian, S., Baratchi, M., Afsharchi, M., Crestani, F.: Lglmf: Local geographical based logistic matrix factorization model for poi recommendation. In: Information Retrieval Technology: 15th Asia Information Retrieval Societies Conference, AIRS 2019, Hong Kong, China, November 7–9, 2019, Proceedings. vol. 12004, p. 66. Springer Nature (2020)
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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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Wan, M., Ni, J., Misra, R., McAuley, J.: Addressing marketing bias in product recommendations. In: Proceedings of the 13th International Conference on Web Search and Data Mining. pp. 618–626 (2020)
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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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2022
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2022
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Gómez, E., Boratto, L., Salamó, M.: Provider fairness across continents in collaborative recommender systems. Information Processing & Management 59
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