Longitudinal Impact of Preference Biases on Recommender Systems’ Performance

References

• Abdollahpouri H, Burke R, Mobasher B (2017) Controlling popularity bias in learning-to-rank recommendation. Proc. 11th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 42–46.Google Scholar
• Abdollahpouri H, Mansoury M, Burke R, Mobasher B (2020) The connection between popularity bias, calibration, and fairness in recommendation. Proc. 14th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 726–731.Google Scholar
• Adomavicius G, Kwon Y (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput. 26(2):351–369.Link, Google Scholar
• Adomavicius G, Bockstedt J, Curley S, Zhang J (2022) Effects of personalized recommendations vs. aggregate ratings on post-consumption preference responses. Management Inform. Systems Quart. 46(1):627–643.Crossref, Google Scholar
• Adomavicius G, Bockstedt J, Curley SP, Zhang J (2019) Reducing recommender system biases: An investigation of rating display designs. Management Inform. Systems Quart. 43(4):1321–1341.Crossref, Google Scholar
• Adomavicius G, Bockstedt JC, Curley SP, Zhang J (2013) Do recommender systems manipulate consumer preferences? A study of anchoring effects. Inform. Systems Res. 24(4):956–975.Link, Google Scholar
• Adomavicius G, Bockstedt JC, Curley SP, Zhang J (2018) Effects of online recommendations on consumers’ willingness to pay. Inform. Systems Res. 29(1):84–102.Link, Google Scholar
• Amatriain X, Pujol JM, Oliver N (2009) I like it… I like it not: Evaluating user ratings noise in recommender systems. Houben GJ, McCalla G, Pianesi F, Zancanaro M, eds. Internat. Conf. on User Modeling, Adaptation, and Personalization (Springer, Berlin), 247–258.Google Scholar
• Baeza-Yates R (2018) Bias on the web. Commun. ACM 61(6):54–61.Crossref, Google Scholar
• Bennett J, Lanning S (2007) The Netflix prize. Proc. KDD Cup and Workshop 2007 (Association for Computing Machinery, New York), 35.Google Scholar
• Casey BM, McIntire DD, Leveno KJ (2001) The continuing value of the Apgar score for the assessment of newborn infants. N. Engl. J. Med. 344(7):467–471.Crossref, Google Scholar
• Chaney AJ, Stewart BM, Engelhardt BE (2018) How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. Proc. 12th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 224–232.Google Scholar
• Chen J, Dong H, Wang X, Feng F, Wang M, He X (2020) Bias and debias in recommender system: A survey and future directions. Preprint, submitted October 7, https://arxiv.org/abs/2010.03240.Google Scholar
• Collins A, Tkaczyk D, Aizawa A, Beel J (2018) Position bias in recommender systems for digital libraries. Internat. Conf. on Inform. (Springer, Cham, Switzerland), 335–344.Google Scholar
• Cosley D, Lam SK, Albert I, Konstan JA, Riedl J (2003) Is seeing believing? How recommender system interfaces affect users’ opinions. Proc. SIGCHI Conf. on Human Factors in Comput. Systems (Association for Computing Machinery, New York), 585–592.Google Scholar
• Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. Proc. Fourth ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 39–46.Google Scholar
• Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, et al. (2010) The YouTube video recommendation system. Proc. Fourth ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 293–296.Google Scholar
• De P, Hu Y, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.Link, Google Scholar
• Dean B (2022) We analyzed 4 million Google search results. Accessed March 1, 2023, https://backlinko.com/google-ctr-stats.Google Scholar
• Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans. Inform. Systems 22(1):143–177.Crossref, Google Scholar
• Ekstrand MD, Burke R, Diaz F (2019) Fairness and discrimination in recommendation and retrieval. Proc. 13th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 576–577.Google Scholar
• Ferraro A, Jannach D, Serra X (2020) Exploring longitudinal effects of session-based recommendations. Proc. 14th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 474–479.Google Scholar
• Flaxman S, Goel S, Rao JM (2016) Filter bubbles, echo chambers, and online news consumption. Public Opin. Quart. 80(S1):298–320.Crossref, Google Scholar
• Fleder D, Hosanagar K (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Sci. 55(5):697–712.Link, Google Scholar
• Funk S (2006) Netflix update: Try this at home. Accessed October 10, 2020, https://sifter.org/∼simon/journal/20061211.html.Google Scholar
• Gao R, Shah C (2020) Counteracting bias and increasing fairness in search and recommender systems. Proc. 14th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 745–747.Google Scholar
• Ge Y, Zhao S, Zhou H, Pei C, Sun F, Ou W, Zhang Y (2020) Understanding echo chambers in e-commerce recommender systems. Proc. 43rd Internat. ACM SIGIR Conf. (Association for Computing Machinery, New York), 2261–2270.Google Scholar
• Gomez-Uribe CA, Hunt N (2015) The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Management Inform. Systems 6(4):1–19.Crossref, Google Scholar
• Hale J (2021) Tiktok’s recommendation algorithm is even more powerful—and potentially dangerous—than YouTube’s. Accessed March 1, 2023, https://www.tubefilter.com/2021/07/21/tiktok-recommendation-algorithm-wall-street-journal-guillaume-chaslot/.Google Scholar
• Hofmann K, Schuth A, Bellogin A, de Rijke M (2014) Effects of position bias on click-based recommender evaluation. European Conf. on Inform. Retrieval (Springer, Berlin), 624–630.Google Scholar
• Järvelin K, Kekäläinen J (2017) IR evaluation methods for retrieving highly relevant documents. ACM SIGIR Forum 51(2):243–250.Crossref, Google Scholar
• Karypis G (2001) Evaluation of item-based top-N recommendation algorithms. Proc. Tenth Internat. Conf. on Inform. and Knowledge Management (Association for Computing Machinery, New York), 247–254.Google Scholar
• Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. Proc. 14th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 426–434.Google Scholar
• Koren Y (2010) Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans. Knowledge Discov. Data 4(1):1–24.Crossref, Google Scholar
• Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37.Crossref, Google Scholar
• Kunaver M, Požrl T (2017) Diversity in recommender systems—A survey. Knowledge-Based Systems 123:154–162.Crossref, Google Scholar
• Larrick RP (2004) Debiasing. Koehler DJ, Harvey N, eds. Blackwell Handbook of Judgment and Decision Making (Blackwell Publishing Ltd, Oxford, UK), 316–337.Crossref, Google Scholar
• Lee D, Hosanagar K (2019) How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Inform. Systems Res. 30(1):239–259.Link, Google Scholar
• Leonard D (2016) Spotify is perfecting the art of the playlist. Accessed October 27, 2023, https://www.bloomberg.com/news/articles/2016-09-21/spotify-is-perfecting-the-art-of-the-playlist.Google Scholar
• Marshall M (2006) Aggregate knowledge raises $5m from Kleiner, on a roll. Accessed October 10, 2020, https://venturebeat.com/2006/12/10/aggregate-knowledge-raises-5m-from-kleiner-on-a-roll/.Google Scholar
• Pariser E (2011) The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think (Penguin Press, New York).Google Scholar
• Prawesh S, Padmanabhan B (2014) The “most popular news” recommender: Count amplification and manipulation resistance. Inform. Systems Res. 25(3):569–589.Link, Google Scholar
• Ricci F, Rokach L, Shapira B (2010) Introduction to recommender systems handbook. Ricci F, Rokach L, Shapira B, Kantor P, eds. Recommender Systems Handbook (Springer, Boston), 1–35.Google Scholar
• Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Proc. 10th Internat. Conf. on World Wide Web (Association for Computing Machinery, New York), 285–295.Google Scholar
• Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. Proc. 24th Internat. Conf. on World Wide Web (Association for Computing Machinery, New York), 111–112.Google Scholar
• Senecal S, Nantel J (2004) The influence of online product recommendations on consumers’ online choices. J. Retailing 80(2):159–169.Crossref, Google Scholar
• Sinha A, Gleich DF, Ramani K (2016) Deconvolving feedback loops in recommender systems. Lee DD, Sugiyama M, Luxburg Uv, Guyon I, Garnett R, eds. Adv. Neural Inform. Processing Systems (NeurIPS, Barcelona, Spain), 3243–3251.Google Scholar
• Soll JB, Milkman KL, Payne JW (2015) A user’s guide to debiasing. Keren G, Wu G, eds. The Wiley Blackwell Handbook of Judgment and Decision Making, vol. II (John Wiley & Sons, Ltd, Chichester, UK), 924–951.Crossref, Google Scholar
• Steck H (2011) Item popularity and recommendation accuracy. Proc. Fifth ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 125–132.Google Scholar
• Sun W, Khenissi S, Nasraoui O, Shafto P (2019) Debiasing the human-recommender system feedback loop in collaborative filtering. Proc. 2019 World Wide Web Conf. (Association for Computing Machinery, New York), 645–651.Google Scholar
• Theocharous G, Thomas PS, Ghavamzadeh M (2015) Personalized ad recommendation systems for life-time value optimization with guarantees. Proc. 24th Internat. Joint Conf. on Artificial Intelligence (AAAI Press, Washington, DC), 1806–1812.Google Scholar
• Wang T, Wang D, Wang F (2014) Quantifying herding effects in crowd wisdom. Proc. 20th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 1087–1096.Google Scholar
• Zeng C, Wang Q, Mokhtari S, Li T (2016) Online context-aware recommendation with time varying multi-armed bandit. Proc. 22nd ACM Internat. Conf. on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 2025–2034.Google Scholar
• Zhang J, Adomavicius G, Gupta A, Ketter W (2020) Consumption and performance: Understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Inform. Systems Res. 31(1):76–101.Link, Google Scholar
• Zhang X, Xie H, Zhao J, Lui JC (2019) Understanding assimilation-contrast effects in online rating systems: Modelling, debiasing, and applications. ACM Trans. Inform. Systems 38(1):1–25.Google Scholar