Eliminating Social Popularity Bias in Recommendation: Causal Inference-Based Social Graph Neural Networks
References
- (2016) Some new estimates of the ‘Jensen gap’. J. Inequality Appl. 2016:1–9.Google Scholar
- (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput. 26(2):351–369.Link, Google Scholar
- (2015) Optimal greedy diversity for recommendation. Proc. 24th Internat. Joint Conf. Artificial Intelligence (Association for Computing Machinery, New York), 1742–1748.Google Scholar
- (2015) Do your online friends make you pay? A randomized field experiment on peer influence in online social networks. Management Sci. 61(8):1902–1920.Link, Google Scholar
- (2018) Causal embeddings for recommendation. Proc. 12th ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 104–112.Google Scholar
- (2021) Integrating individual and aggregate diversity in top-n recommendation. INFORMS J. Comput. 33(1):300–318.Link, Google Scholar
- (1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proc. 21st Ann. Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 335–336.Google Scholar
- (2023) Bias and debias in recommender system: A survey and future directions. ACM Trans. Inform. Systems 41(3):1–39.Crossref, Google Scholar
- (2020) ESAM: Discriminative domain adaptation with non-displayed items to improve long-tail performance. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 579–588.Google Scholar
- (2004) Social influence: Compliance and conformity. Ann. Rev. Psych. 55:591–621.Crossref, Google Scholar
- (2021) The echo chamber effect on social media. Proc. Natl. Acad. Sci. USA 118(9):e2023301118.Crossref, Google Scholar
- (2017) Popularity or proximity: Characterizing the nature of social influence in an online music community. Inform. Systems Res. 28(1):117–136.Link, Google Scholar
- (2000) The problem of information overload in business organisations: A review of the literature. Internat. J. Inform. Management 20(1):17–28.Crossref, Google Scholar
- (2019) Graph neural networks for social recommendation. Proc. World Wide Web Conf. (Association for Computing Machinery, New York), 417–426.Google Scholar
- (2020) A social-semantic recommender system for advertisements. Inform. Processing Management 57(2):102153.Crossref, Google Scholar
- (2020) Understanding echo chambers in E-commerce recommender systems. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Informat. Retrieval (Association for Computing Machinery, New York), 2261–2270.Google Scholar
- (2010) Anatomy of the long tail: Ordinary people with extraordinary tastes. Proc. 3rd ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 201–210.Google Scholar
- (2015) TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. Proc. 29th AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, California), 123–129.Google Scholar
- (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 639–648.Google Scholar
- (2017) Neural collaborative filtering. Proc. 26th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 173–182.Google Scholar
- (2022) Deconfounded visual grounding. Proc. 36th AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, California), 998–1006.Google Scholar
- (2011) Opinion leadership and social contagion in new product diffusion. Marketing Sci. 30(2):195–212.Link, Google Scholar
- (2010) A matrix factorization technique with trust propagation for recommendation in social networks. Proc. 4th ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 135–142.Google Scholar
- (2004) The effect of information overload on consumer choice quality in an on-line environment. Psych. Marketing 21(3):159–183.Crossref, Google Scholar
- (2024) Unlocking the power of peer influence: Strategies for bridging the adoption chasm in new product diffusion. Managerial Decision Econom. 46(1):361–377.Crossref, Google Scholar
- (2008) SoRec: Social recommendation using probabilistic matrix factorization. Proc. 17th ACM Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 931–940.Google Scholar
- (2013) Social influence bias: A randomized experiment. Science 341(6146):647–651.Crossref, Google Scholar
- (2009) Causality (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2018) The Book of Why: The New Science of Cause and Effect (Hachette UK, London).Google Scholar
- (2010) Factorization machines. Proc. IEEE Internat. Conf. Data Mining (Institute of Electrical and Electronics Engineers, New York), 995–1000.Google Scholar
- (2014) BPR: Bayesian personalized ranking from implicit feedback. Proc. 25th Uncertainty Artificial Intelligence (Association for Uncertainty in Artificial Intelligence, New York), 452–461.Google Scholar
- (2016) Recommendations as treatments: Debiasing learning and evaluation. Proc. Internat. Conf. Machine Learn. (Proceedings of Machine Learning Research, New York), 1670–1679.Google Scholar
- (2021) Social recommendation with implicit social influence. Proc. 44th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 1788–1792.Google Scholar
- (1992) Building an information system design theory for vigilant EIS. Inform. Systems Res. 3(1):36–59.Link, Google Scholar
- (2021a) Deconfounded recommendation for alleviating bias amplification. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1717–1725.Google Scholar
- (2021b) Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. Proc. 44th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 1288–1297.Google Scholar
- (2020) Information theoretic counterfactual learning from missing-not-at-random feedback. Adv. Neural Inform. Processing Systems 33:1854–1864.Google Scholar
- (2024) Dynamic Bayesian network–based product recommendation considering consumers’ multistage shopping journeys: A marketing funnel perspective. Inform. Systems Res. 35(3):1382–1402.Link, Google Scholar
- (2021) Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1791–1800.Google Scholar
- (2019) A hierarchical attention model for social contextual image recommendation. IEEE Trans. Knowledge Data Engrg. 32(10):1854–1867.Crossref, Google Scholar
- (2025) Eliminating social popularity bias in recommendation: Causal inference-based social graph neural networks. https://doi.org/10.1287/ijoc.2024.0682.cd, https://github.com/INFORMSJoC/2024.0682.Google Scholar
- (2012) Alternate strategies for a win-win seeking agent in agent-human negotiations. J. Management Inform. Systems 29(3):223–256.Crossref, Google Scholar
- (2023) Coping with homogeneous information flow in recommender systems: Algorithmic resistance and avoidance. Internat. J. Human–Comput. Interaction 40(22):6899–6912.Google Scholar
- (2021) Causal intervention for leveraging popularity bias in recommendation. Proc. 44th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 11–20.Google Scholar
- (2024) Fairness and diversity in recommender systems: A survey. ACM Trans. Intelligent System Tech. 16(1):1–28.Google Scholar
- (2022) Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation. IEEE Trans. Knowledge Data Engrg. 35(10):9920–9931.Crossref, Google Scholar
- (2021) Disentangling user interest and conformity for recommendation with causal embedding. Proc. Web Conf. (Association for Computing Machinery, New York), 2980–2991.Google Scholar
- (2021a) Popularity bias in dynamic recommendation. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 2439–2449.Google Scholar
- (2021b) Popularity-opportunity bias in collaborative filtering. Proc. 14th ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 85–93.Google Scholar

