Skip to main content

Advertisement

Springer Nature Link
Log in

Graph-induced rank-aggregation using information fusion operators

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Rank-aggregation, the process of fusing multiple ranked lists into a single unified ranking, is a cornerstone of various information retrieval systems. Given the widespread adoption of these techniques and the increasing demand for precise and relevant results, the development of innovative rank aggregation algorithms is imperative. This paper proposes a novel rank-aggregation algorithm which leverages graph theory and information fusion to model inter-element relationships and combine ranked lists effectively. By constructing a rankers’ similarity graph and employing spectral clustering, the algorithm identifies top-performing rankers, whose rankings are combined using the random forest algorithm, which is applied several times to produce different combined rankings and score them based on the average expertise of included rankers. Finally, information fusion theory is employed to aggregate the ensemble of rankers’ outputs through the optimistic and pessimistic exponential ordered weighted averaging operators. Experimental results on LETOR4.0 datasets demonstrate noticeable improvements compared to baselines, highlighting the efficiency of the proposed algorithm using only a limited number of rankers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

We used LETOR4.0 dataset as a publicly available dataset which could be acessed via: https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/letor-4-0/.

References

  1. Liu TY (2009) Learning to rank for information retrieval. Found Trends Inf Retr 3:225–231. https://doi.org/10.1561/1500000016

    Article  Google Scholar 

  2. Li H (2015) Learning to rank for information retrieval and natural language processing. Synthesis lectures on human language technologies 7 1–123. https://doi.org/10.2200/S00607ED2V01Y201410HLT026/SUPPL_FILE/LI_CH1.PDF

  3. Tan X, Yu W, Tan L (2024) Large-scale rank aggregation from multiple data sources based d3mopso method. Web and big data. Springer, Cham, pp 63–80

    Chapter  Google Scholar 

  4. Pujari M, Kanawati R (2012) Supervised rank aggregation approach for link prediction in complex networks. In: WWW ’12 companion: Proceedings of the 21st International Conference on World Wide Web. 1189–1196

  5. Chakraborty D, Das S, Khan A, Subramanian A (2022) Fair rank aggregation. Adv Neural Inf Process Syst 35:23965–23978

    Google Scholar 

  6. Wei D, Islam MM, Schieber B, Basu Roy S (2022) Rank aggregation with proportionate fairness. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery. 262–275

  7. Zhu W, Jiang Y, Liu JS, Deng K (2023) Partition-mallows model and its inference for rank aggregation. J Am Stat Assoc 118:343–359. https://doi.org/10.1080/01621459.2021.1930547

    Article  MathSciNet  Google Scholar 

  8. Xu S, Sun WW, Cheng G (2024) Rate-optimal rank aggregation with private pairwise rankings

  9. Keyhanipour AH, Oroumchian F (2021) Click models inspired learning to rank. Int J Web Inf Syst 17:261–286. https://doi.org/10.1108/IJWIS-03-2021-0017

    Article  Google Scholar 

  10. Keyhanipour AH, Moshiri B, Rahgozar M et al (2016) Integration of data fusion and reinforcement learning techniques for the rank-aggregation problem. Int J Mach Learn Cybern 7:1131–1145. https://doi.org/10.1007/S13042-015-0442-6/TABLES/8

    Article  Google Scholar 

  11. Keyhanipour AH (2023) Graph-based comparative analysis of learning to rank datasets. Int J Data Sci Anal. https://doi.org/10.1007/S41060-023-00406-8/METRICS

    Article  Google Scholar 

  12. Newman M (2018) Networks: a introduction, 2nd edn. Oxford University Press

  13. Filev D, Yager RR (1994) Learning OWA operator weights from data. IEEE Int Conf Fuzzy Syst 1:468–473. https://doi.org/10.1109/FUZZY.1994.343740

    Article  Google Scholar 

  14. Liu T-Y, Xu J, Qin T, et al (2007) LETOR : benchmark dataset for research on learning to rank for information retrieval. In: Proceedings of the SIGIR workshop on learning to rank for information retrieval. Institute of electronics, information and communication, Engineers, IEICE. 1854–1862

  15. Emerson P (2016) From majority rule to inclusive politics: Electing a power-sharing coalition. Springer International Publishing

  16. Colley R, Grandi U, Hidalgo C et al (2023) Measuring and controlling divisiveness in rank aggregation. arXiv: 230608511

  17. Alabi D, Ghazi B, Kumar R, Manurangsi P (2022) Private rank aggregation in central and local models. Proceed AAAI Conf Artif Intell 36:5984–5991. https://doi.org/10.1609/AAAI.V36I6.20544

    Article  Google Scholar 

  18. Hu C, Zhang H, Liang C, Huang H (2024) QI-IRA: quantum-inspired interactive ranking aggregation for person re-identification. Proceed AAAI Conf Artif Intell 38:2202–2210. https://doi.org/10.1609/AAAI.V38I3.27993

    Article  Google Scholar 

  19. Dall’Agnol M, deCarvalho VO (2024) AC. Rank: rule ranking method via aggregation of objective measures for associative classifiers. IEEE Access 12:88862–88882. https://doi.org/10.1109/ACCESS.2024.3419130

    Article  Google Scholar 

  20. Balogun AO, Basri S, Mahamad S et al (2021) Empirical analysis of rank aggregation-based multi-filter feature selection methods in software defect prediction. Electronics 10:179. https://doi.org/10.3390/ELECTRONICS10020179

    Article  Google Scholar 

  21. Alvin YHY, Chakraborty D (2023) Approximate maximum rank aggregation: beyond the worst-case. In: Leibniz International Proceedings in Informatics, LIPIcs. 284 https://doi.org/10.4230/LIPICS.FSTTCS.2023.12/-/STATS

  22. Kuhlman C, Rundensteiner E (2020) Rank aggregation algorithms for fair consensus. Proceed VLDB End 13:2706–2719. https://doi.org/10.14778/3407790.3407855

    Article  Google Scholar 

  23. Andrieu P, Cohen-Boulakia S, Couceiro M et al (2023) A unifying rank aggregation framework to suitably and efficiently aggregate any kind of rankings. Int J Approx Reason 162:109035. https://doi.org/10.1016/J.IJAR.2023.109035

    Article  MathSciNet  Google Scholar 

  24. Abdolrazzagh-Nezahd M, Kherad M, Abdolrazzagh-Nezhad M (2023) Weighted rank aggregation based on ranker accuracies for feature selection. Soft comput. https://doi.org/10.21203/RS.3.RS-2213061/V1

    Article  Google Scholar 

  25. Xiao Y, Zhu H, Chen D et al (2023) Measuring robustness in rank aggregation based on the error-effectiveness curve. Inf Process Manag 60:103355. https://doi.org/10.1016/J.IPM.2023.103355

    Article  Google Scholar 

  26. Ma K, Xu Q, Zeng J et al (2024) Sequential manipulation against rank aggregation: theory and algorithm. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2024.3416710

    Article  Google Scholar 

  27. Jarman B, Kassab L, Needell D, Sietsema A (2024) Stochastic iterative methods for online rank aggregation from pairwise comparisons. BIT Numer Math 64:1–21. https://doi.org/10.1007/S10543-024-01024-X/METRICS

    Article  MathSciNet  Google Scholar 

  28. Ding J, Han D, Dezert J, Yang Y (2018) A new hierarchical ranking aggregation method. Inf Sci (N Y) 453:168–185. https://doi.org/10.1016/J.INS.2018.04.041

    Article  MathSciNet  Google Scholar 

  29. Jin T, Xu P, Gu Q, Farnoud F (2020) Rank aggregation via heterogeneous thurstone preference models. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI press 4353–4360

  30. Liang S, Markov I, Ren Z, De Rijke M (2018) Manifold learning for rank aggregation. In: Proceedings of the World Wide Web Conference, WWW 2018. ACM 1735–1744

  31. Akritidis L, Fevgas A, Bozanis P, Manolopoulos Y (2022) An unsupervised distance-based model for weighted rank aggregation with list pruning. Expert Syst Appl 202:117435. https://doi.org/10.1016/J.ESWA.2022.117435

    Article  Google Scholar 

  32. Muravyov SV, Khudonogova LI, Emelyanova EY (2018) Interval data fusion with preference aggregation. Measurement 116:621–630. https://doi.org/10.1016/J.MEASUREMENT.2017.08.045

    Article  Google Scholar 

  33. Muravyov SV, Emelyanova EY (2021) Kemeny rule for preference aggregation: Reducing all exact solutions to a single one. Measurement 182:109403. https://doi.org/10.1016/J.MEASUREMENT.2021.109403

    Article  Google Scholar 

  34. Venkatesh B, Anuradha J (2020) A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data. Int J Knowl-based Intell Eng Syst 24:289–301. https://doi.org/10.3233/KES-190134

    Article  Google Scholar 

  35. Singh M, Pant M, Diwan S, Snášel V (2022) Genetic algorithm-enhanced rank aggregation model to measure the performance of pulp and paper industries. Comput Ind Eng 172:108548. https://doi.org/10.1016/J.CIE.2022.108548

    Article  Google Scholar 

  36. Kujawska HM (2019) Machine learning methods for preference aggregation. The University of Bergen

  37. Bałchanowski M, Boryczka U (2023) A comparative study of rank aggregation methods in recommendation systems. Entropy 25:132. https://doi.org/10.3390/E25010132

    Article  Google Scholar 

  38. Chen D, Xiao Y, Zhu H et al (2023) Robustness of rank aggregation methods for malicious disturbance. Inf Sci (N Y) 624:639–651. https://doi.org/10.1016/J.INS.2023.01.008

    Article  Google Scholar 

  39. Korba A, Clemencon S, Sibony E (2017) A learning theory of ranking aggregation. Proc Mach Learn Res 54:1001–1010

    Google Scholar 

  40. Zhang Y, Cheung YM, Tan KC (2020) A unified entropy-based distance metric for ordinal-and-nominal-attribute data clustering. IEEE Trans Neural Netw Learn Syst 31:39–52. https://doi.org/10.1109/TNNLS.2019.2899381

    Article  Google Scholar 

  41. Bertoni M, Nisticò R (2023) Ordinal rank and the structure of ability peer effects. J Public Econ 217:104797. https://doi.org/10.1016/J.JPUBECO.2022.104797

    Article  Google Scholar 

  42. Zhang Y, Cheung YM (2022) Learnable weighting of intra-attribute distances for categorical data clustering with nominal and ordinal attributes. IEEE Trans Pattern Anal Mach Intell 44:3560–3576. https://doi.org/10.1109/TPAMI.2021.3056510

    Article  Google Scholar 

  43. Dopazo E, Martínez-Céspedes ML (2017) Rank aggregation methods dealing with ordinal uncertain preferences. Expert Syst Appl 78:103–109. https://doi.org/10.1016/J.ESWA.2017.01.051

    Article  Google Scholar 

  44. Zhang Y, Cheung YM (2022) A new distance metric exploiting heterogeneous interattribute relationship for ordinal-and-nominal-attribute data clustering. IEEE Trans Cybern 52:758–771. https://doi.org/10.1109/TCYB.2020.2983073

    Article  Google Scholar 

  45. D’Ambrosio A, Iorio C, Staiano M, Siciliano R (2019) Median constrained bucket order rank aggregation. Comput Stat 34:787–802. https://doi.org/10.1007/S00180-018-0858-Z/METRICS

    Article  MathSciNet  Google Scholar 

  46. Zhang Y, Cheung YM (2020) An ordinal data clustering algorithm with automated distance learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. 34 6869–6876 https://doi.org/10.1609/AAAI.V34I04.6168

  47. Wang H, Peng Y, Kou G (2021) A two-stage ranking method to minimize ordinal violation for pairwise comparisons. Appl Soft Comput 106:107287. https://doi.org/10.1016/J.ASOC.2021.107287

    Article  Google Scholar 

  48. Zhang Y, Cheung Y-M, Zeng A (2022) Het2Hom: representation of heterogeneous attributes into homogeneous concept spaces for categorical-and-numerical-attribute data clustering. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-22). 3758–3765

  49. Zhang Y, Cheung YM (2023) Graph-based dissimilarity measurement for cluster analysis of any-type-attributed data. IEEE Trans Neural Netw Learn Syst 34:6530–6544. https://doi.org/10.1109/TNNLS.2022.3202700

    Article  MathSciNet  Google Scholar 

  50. Liu N, Xu Z, Zeng XJ, Ren P (2021) An agglomerative hierarchical clustering algorithm for linear ordinal rankings. Inf Sci (N Y) 557:170–193. https://doi.org/10.1016/J.INS.2020.12.056

    Article  MathSciNet  Google Scholar 

  51. Chen J, Ji Y, Zou R, et al (2024) QGRL: quaternion graph representation learning for heterogeneous feature data clustering. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 297–306

  52. Cape J, Yu X, Liao JZ (2024) Robust spectral clustering with rank statistics. ArXiv arXiv:2408.10136. https://doi.org/10.48550/ARXIV.2408.10136

  53. Wang P, Zhang Y, Zhang Y, et al (2024) Clustering by learning the ordinal relationships of qualitative attribute values. In: Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers (IEEE). 1–8

  54. Keyhanipour AH (2024) Learning to rank through graph-based feature fusion using fuzzy integral operators. Appl Intell 54:11914–11932. https://doi.org/10.1007/S10489-024-05755-W/METRICS

    Article  Google Scholar 

  55. Yeh JY, Tsai CJ (2022) A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased pagerank for relevance analysis. Comput Sci Inf Syst 19:141–164. https://doi.org/10.2298/CSIS201220042Y

    Article  Google Scholar 

  56. Yeh JY, Tsai CJ (2020) Graph-based feature selection method for learning to rank. In: ACM International Conference Proceeding Series. https://doi.org/10.1145/3442555.3442567

  57. Keyhanipour AH, Moshiri B, Rahgozar M (2015) CF-Rank: Learning to rank by classifier fusion on click-through data. Expert Syst Appl 42:8597–8608. https://doi.org/10.1016/J.ESWA.2015.07.014

    Article  Google Scholar 

  58. Barabási A-L, Pósfai M (2016) Network science, 1st edn. Cambridge University Press

  59. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  60. Büttcher Stefan, Clarke CLA 1964-, Cormack GV (2016) Information retrieval: implementing and evaluating search engines. MIT Press

  61. Qin T, Liu T-Y (2009) LETOR: learning to rank for information retrieval: LETOR 4.0-microsoft research. In: microsoft. https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/letor-4-0/. Accessed 29 Jun 2024

  62. Qin T, Geng X, Liu T (2010) A new probabilistic model for rank aggregation. Adv Neural Inf Process Syst 23

  63. Gleich DF, Lim LH (2011) Rank aggregation via nuclear norm minimization. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 60–68

  64. Volkovs MN, Larochelle H, Zemel RS (2012) Learning to rank by aggregating expert preferences. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 843–851

  65. Cormack G V., Clarke CLA, Buettcher S (2009) Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 758–759

  66. Guiver J, Snelson E (2009) Bayesian inference for Plackett-Luce ranking models. In: Proceedings of the 26th Annual International Conference on Machine Learning

  67. Volkovs MN, Zemel RS (2013) CRF framework for supervised preference aggregation. In: International Conference on Information and Knowledge Management, Proceedings 89–98. https://doi.org/10.1145/2505515.2505713

  68. Liu H, Du Y, Wu Z (2022) Generalized ambiguity decomposition for ranking ensemble learning. J Mach Learn Res 23:1–36

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Faculty of Engineering, College of Farabi, University of Tehran, Qom, Iran

    Amir Hosein Keyhanipour

Authors
  1. Amir Hosein Keyhanipour
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The author, Amir Hosein Keyhanipour, confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Corresponding author

Correspondence to Amir Hosein Keyhanipour.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keyhanipour, A.H. Graph-induced rank-aggregation using information fusion operators. J Supercomput 81, 43 (2025). https://doi.org/10.1007/s11227-024-06595-8

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06595-8

Keywords