Abstract
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., Last.fm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.
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References
Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 42–46 (2017)
Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking. In: The Thirty-second International Flairs Conference (2019)
Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. In: RecSys Workshop on Recommendation in Multistakeholder Environments (RMSE) (2019)
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)
Adamopoulos, P., Tuzhilin, A.: On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 153–160 (2014)
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2011)
Antikacioglu, A., Ravi, R.: Post processing recommender systems for diversity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 707–716 (2017)
Baeza-Yates, R.: Bias in search and recommender systems. In: Fourteenth ACM Conference on Recommender Systems, p. 2 (2020)
Bauer, C., Schedl, M.: Global and country-specific mainstreaminess measures: definitions, analysis, and usage for improving personalized music recommendation systems. PLoS One 14(6), e0217389 (2019)
Brynjolfsson, E., Hu, Y.J., Smith, M.D.: From niches to riches: anatomy of the long tail. Sloan Manage. Rev. 47(4), 67–71 (2006)
Deldjoo, Y., Schedl, M., Cremonesi, P., Pasi, G.: Recommender systems leveraging multimedia content. ACM Comput. Surv. (CSUR) 53(5), 1–38 (2020)
George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), p. 4. IEEE (2005)
Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User Adapt. Interact. 25(5), 427–491 (2015). https://doi.org/10.1007/s11257-015-9165-3
Kowald, D., Lex, E.: The influence of frequency, recency and semantic context on the reuse of tags in social tagging systems. In: Proceedings of Hypertext 2016, pp. 237–242. ACM, New York, NY, USA (2016)
Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M., Lex, E.: Support the underground: characteristics of beyond-mainstream music listeners. EPJ Data Sci. 10(1), 1–26 (2021). https://doi.org/10.1140/epjds/s13688-021-00268-9
Kowald, D., Schedl, M., Lex, E.: The unfairness of popularity bias in music recommendation: a reproducibility study. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 35–42. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_5
Lacic, E., Kowald, D., Seitlinger, P.C., Trattner, C., Parra, D.: Recommending items in social tagging systems using tag and time information. In: Proceedings of the 1st Social Personalization Workshop co-located with the 25th ACM Conference on Hypertext and Social Media, pp. 4–9. ACM (2014)
Lacic, E., Kowald, D., Traub, M., Luzhnica, G., Simon, J.P., Lex, E.: Tackling cold-start users in recommender systems with indoor positioning systems. In: Poster Proceedings of the 9th ACM Conference on Recommender Systems. Association of Computing Machinery (2015)
Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Indust. Inform. 10(2), 1273–1284 (2014)
Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 11–18 (2008)
Seitlinger, P., Kowald, D., Kopeinik, S., Hasani-Mavriqi, I., Lex, E., Ley, T.: Attention please! a hybrid resource recommender mimicking attention-interpretation dynamics. In: Proceedings of WWW 2015 companion, pp. 339–345. ACM (2015)
Shi, Y., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. 47(1), 3:1–3:45 (2014)
Sun, W., Khenissi, S., Nasraoui, O., Shafto, P.: Debiasing the human-recommender system feedback loop in collaborative filtering. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 645–651 (2019)
Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fa* ir: A fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1569–1578 (2017)
Acknowledgements
This research was funded by the H2020 project TRUSTS (GA: 871481) and the “DDAI” COMET Module within the COMET - Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG.
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Kowald, D., Lacic, E. (2022). Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham. https://doi.org/10.1007/978-3-031-09316-6_1
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