Which to Trust: Extracting Collective Wisdom Based on Opinion Quality Rank Learning

Published Online:https://doi.org/10.1287/ijoc.2024.0835

References

  • Adomavicius G, Wang Y (2022) Improving reliability estimation for individual numeric predictions: A machine learning approach. INFORMS J. Comput. 34(1):503–521.LinkGoogle Scholar
  • Budescu DV, Chen E (2015) Identifying expertise to extract the wisdom of crowds. Management Sci. 61(2):267–280.LinkGoogle Scholar
  • Burges CJ (2010) From ranknet to lambdarank to lambdamart: An overview. Learning 11(581):23–81.Google Scholar
  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. Proc. 22nd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 89–96.Google Scholar
  • Cao Z, Qin T, Liu TY, Tsai MF, Li H (2007) Learning to rank: From pairwise approach to listwise approach. Proc. 24th Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 129–136.Google Scholar
  • Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, et al. (2016) Wide & deep learning for recommender systems. Proc. 1st Workshop Deep Learn. Recommender Systems (Association for Computing Machinery, New York), 7–10.Google Scholar
  • Da Z, Huang X (2020) Harnessing the wisdom of crowds. Management Sci. 66(5):1847–1867.LinkGoogle Scholar
  • David HA (1987) Ranking from unbalanced paired-comparison data. Biometrika 74(2):432–436.CrossrefGoogle Scholar
  • De Bacco C, Larremore DB, Moore C (2018) A physical model for efficient ranking in networks. Sci. Adv. 4(7):eaar8260.CrossrefGoogle Scholar
  • Ertimur Y, Sunder J, Sunder SV (2007) Measure for measure: The relation between forecast accuracy and recommendation profitability of analysts. J. Accounting Res. 45(3):567–606.CrossrefGoogle Scholar
  • Fogel F, d’Aspremont A, Vojnovic M (2016) Spectral ranking using seriation. J. Machine Learn. Res. 17(88):1–45.Google Scholar
  • Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: A factorization-machine based neural network for ctr prediction. Proc. 26th Internat. Joint Conf. Artificial Intelligence (AAAI Press, Melbourne, Australia), 1725–1731.Google Scholar
  • Han J, Liu L (2019) Interlock concentration and analyst forecast accuracy: Value implications of interlock. Australian Accounting Rev. 29(1):64–79.CrossrefGoogle Scholar
  • He Y, Gan Q, Wipf D, Reinert GD, Yan J, Cucuringu M (2022) Gnnrank: Learning global rankings from pairwise comparisons via directed graph neural networks. Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S, eds. Proc. 39th Internat. Conf. Machine Learn., vol. 162 (PMLR, New York), 8581–8612.Google Scholar
  • Huang K (2020) Management forecast errors and corporate investment efficiency. J. Contemporary Accounting Econom. 16(3):100208.CrossrefGoogle Scholar
  • Jiang S, Guo Y, Liu C, Xiong H (2025) Which to trust: Extracting collective wisdom based on opinion quality rank learning. https://doi.org/10.1287/ijoc.2024.0835.cd, https://github.com/INFORMSJoC/2024.0835.Google Scholar
  • Jiang S, Guo Y, Zhou W, Li X (2023) Identifying predictors of analyst rating quality: An ensemble feature selection approach. Internat. J. Forecasting 39(4):1853–1873.CrossrefGoogle Scholar
  • Köppel M, Segner A, Wagener M, Pensel L, Karwath A, Kramer S (2020) Pairwise learning to rank by neural networks revisited: Reconstruction, theoretical analysis and practical performance. Proc. Eur. Conf. Machine Learn. Knowledge Discovery Databases (Springer, Cham, Switzerland), 237–252.CrossrefGoogle Scholar
  • Li Q, Li Y, Gao J, Zhao B, Fan W, Han J (2014) Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. Proc. ACM SIGMOD Internat. Conf. Management Data (Association for Computing Machinery, New York), 1187–1198.Google Scholar
  • Li W, Wu W, Wang H, Cheng X, Chen H, Zhou Z, Ding R (2017) Crowd intelligence in AI 2.0 era. Frontiers Inform. Tech. Electronic Engrg. 18:15–43. CrossrefGoogle Scholar
  • Li Y, Gao J, Meng C, Li Q, Su L, Zhao B, Fan W, et al. (2016) A survey on truth discovery. ACM Sigkdd Explorations Newslett. 17(2):1–16.CrossrefGoogle Scholar
  • Liu AZ, Schneible R (2017) Analysts’ experience and interpretation of discretionary accruals in predicting future earnings. Adv. Accounting 38:88–98.CrossrefGoogle Scholar
  • Luce RD (1959) Individual Choice Behavior (Wiley, New York).Google Scholar
  • Luo C, Xiong H, Zhou W, Guo Y, Deng G (2011) Enhancing investment decisions in p2p lending: An investor composition perspective. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 292–300.Google Scholar
  • Ma F, Li Y, Li Q, Qiu M, Gao J, Zhi S, Su L, et al. (2015) Faitcrowd: Fine grained truth discovery for crowdsourced data aggregation. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 745–754.Google Scholar
  • Maystre L, Grossglauser M (2015) Fast and accurate inference of Plackett–Luce models. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates Inc., Red Hook, NY).Google Scholar
  • McCoy J, Prelec D (2024) A Bayesian hierarchical model of crowd wisdom based on predicting opinions of others. Management Sci. 70(9):5931–5948.AbstractGoogle Scholar
  • Minka TP (2001) Expectation propagation for approximate Bayesian inference. Proc. 17th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco), 362–369.Google Scholar
  • Negahban S, Oh S, Shah D (2016) Rank centrality: Ranking from pair-wise comparisons. Oper. Res. 266–287.Google Scholar
  • Radcliffe K, Lyson HC, Barr-Walker J, Sarkar U (2019) Collective intelligence in medical decision-making: A systematic scoping review. BMC Medical Inform. Decision Making 19:1–11.CrossrefGoogle Scholar
  • Song H, Kim M, Park D, Shin Y, Lee JG (2023) Learning from noisy labels with deep neural networks: A survey. IEEE Trans. Neural Network Learn. Systems 34(11):8135–8153.CrossrefGoogle Scholar
  • Song W, Shi C, Xiao Z, Duan Z, Xu Y, Zhang M, Tang J (2019) Autoint: Automatic feature interaction learning via self-attentive neural networks. Proc. 28th ACM Internat. Conf. Information Knowledge Management (Association for Computing Machinery, New York), 1161–1170.Google Scholar
  • Tian H, Zheng X, Zhao K, Liu MW, Zeng DD (2022) Inductive representation learning on dynamic stock co-movement graphs for stock predictions. INFORMS J. Comput. 34(4):1940–1957.LinkGoogle Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Lu K, Polosukhin I (2017) Attention is all you need. Guyon I, Von Luxburg U, Bengio S, Wallach S, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY). Google Scholar
  • Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. Proc. Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
  • Vinayak RK, Gilad-Bachrach R (2015) Dart: Dropouts meet multiple additive regression trees. Proc. Artificial Intelligence Statist. (PMLR, New York), 489–497.Google Scholar
  • Vojnović M, Yun SY, Zhou K (2023) Accelerated MM algorithms for inference of ranking scores from comparison data. Oper. Res. 71(4):1318–1342.LinkGoogle Scholar
  • Wang R, Fu B, Fu G, Wang M (2017) Deep & cross network for ad click predictions. Proc. ADKDD’17 (Association for Computing Machinery, New York), 1–7.Google Scholar
  • Zhong H, Chen Y, Liu C, Benson H (2024) Decision aggregation with reliability propagation. Decision Support Systems 178:114130.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.