• Zhang C, Chen W, Zhang W and Xu M. (2024). Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items. ACM Transactions on Intelligent Systems and Technology. 10.1145/3653983.

    https://dl.acm.org/doi/10.1145/3653983

  • Yeh J and Tsai C. (2022). A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis. Computer Science and Information Systems. 10.2298/CSIS201220042Y. 19:1. (141-164).

    http://www.doiserbia.nb.rs/Article.aspx?ID=1820-02142100042Y

  • Dammak F and Kammoun H. (2021). Combining semi-supervised and active learning to rank algorithms: application to Document Retrieval. Information Retrieval Journal. 10.1007/s10791-021-09396-2.

    https://link.springer.com/10.1007/s10791-021-09396-2

  • Kuzenkov O, Morozov A and Kuzenkova G. (2020). Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods. Entropy. 10.3390/e23010035. 23:1. (35).

    https://www.mdpi.com/1099-4300/23/1/35

  • Lai Y and Zhen J. (2020). A Ranking Learning Training Method Based on Singular Value Decomposition. Communications, Signal Processing, and Systems. 10.1007/978-981-13-9409-6_144. (1218-1221).

    http://link.springer.com/10.1007/978-981-13-9409-6_144

  • Rahangdale A and Raut S. (2019). Clustering-Based Transductive Semi-Supervised Learning for Learning-to-Rank. International Journal of Pattern Recognition and Artificial Intelligence. 10.1142/S0218001419510078. 33:12. (1951007). Online publication date: 1-Nov-2019.

    https://www.worldscientific.com/doi/abs/10.1142/S0218001419510078

  • e Freitas M, Sousa D, Martins W, Rosa T, Silva R and Gonçalves M. (2018). Parallel rule‐based selective sampling and on‐demand learning to rank. Concurrency and Computation: Practice and Experience. 10.1002/cpe.4464. 31:18. Online publication date: 25-Sep-2019.

    https://onlinelibrary.wiley.com/doi/10.1002/cpe.4464

  • Zhang X, Zhao Z, Liu C, Zhang C and Cheng Z. (2019). Semi-supervised Learning to Rank with Uncertain Data. Web Information Systems and Applications. 10.1007/978-3-030-30952-7_4. (28-39).

    http://link.springer.com/10.1007/978-3-030-30952-7_4

  • Fu M, Qu H, Li F and Liu Y. (2018). A New Deep Neural Network Based Learning to Rank Method for Information Retrieval 2018 IEEE International Conference on Information and Automation (ICIA). 10.1109/ICInfA.2018.8812518. 978-1-5386-8069-8. (1311-1316).

    https://ieeexplore.ieee.org/document/8812518/

  • Albuquerque A, Amador T, Ferreira R, Veloso A and Ziviani N. (2018). Learning to Rank with Deep Autoencoder Features 2018 International Joint Conference on Neural Networks (IJCNN). 10.1109/IJCNN.2018.8489646. 978-1-5090-6014-6. (1-8).

    https://ieeexplore.ieee.org/document/8489646/

  • Chen S, Chen K, Xu C and Lan L. (2018). Flexible ranking extreme learning machine based on matrix-centering transformation 2018 International Joint Conference on Neural Networks (IJCNN). 10.1109/IJCNN.2018.8489418. 978-1-5090-6014-6. (1-8).

    https://ieeexplore.ieee.org/document/8489418/

  • SUEHIRO D, HATANO K and TAKIMOTO E. (2018). Efficient Reformulation of 1-Norm Ranking SVM. IEICE Transactions on Information and Systems. 10.1587/transinf.2017EDP7233. E101.D:3. (719-729).

    https://www.jstage.jst.go.jp/article/transinf/E101.D/3/E101.D_2017EDP7233/_article

  • Dammak F, Kammoun H and Ben Hamadou A. (2017). Improving pairwise learning to rank algorithms for document retrieval 2017 IEEE Symposium Series on Computational Intelligence (SSCI). 10.1109/SSCI.2017.8285374. 978-1-5386-2726-6. (1-8).

    http://ieeexplore.ieee.org/document/8285374/

  • Soldaini L, Yates A and Goharian N. (2017). Learning to reformulate long queries for clinical decision support. Journal of the Association for Information Science and Technology. 68:11. (2602-2619). Online publication date: 1-Nov-2017.

    https://doi.org/10.1002/asi.23924

  • Fang B, Jia Y, Li X, Li A and Wu X. Big Search in Cyberspace. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2017.2699675. 29:9. (1793-1805).

    http://ieeexplore.ieee.org/document/7914645/

  • Chen K, Li R, Dou Y, Liang Z and Lv Q. (2017). Ranking Support Vector Machine with Kernel Approximation. Computational Intelligence and Neuroscience. 2017. (5). Online publication date: 1-Feb-2017.

    https://doi.org/10.1155/2017/4629534

  • Yu A and Grauman K. (2017). Fine-Grained Comparisons with Attributes. Visual Attributes. 10.1007/978-3-319-50077-5_6. (119-154).

    http://link.springer.com/10.1007/978-3-319-50077-5_6

  • Zhang X, He B and Luo T. (2016). Training query filtering for semi-supervised learning to rank with pseudo labels. World Wide Web. 19:5. (833-864). Online publication date: 1-Sep-2016.

    https://doi.org/10.1007/s11280-015-0363-z

  • (2015). A cross-benchmark comparison of 87 learning to rank methods. Information Processing and Management: an International Journal. 51:6. (757-772). Online publication date: 1-Nov-2015.

    https://doi.org/10.1016/j.ipm.2015.07.002

  • Ding W, Geng X and Zhang X. (2015). Learning to Rank from Noisy Data. ACM Transactions on Intelligent Systems and Technology. 7:1. (1-21). Online publication date: 16-Oct-2015.

    https://doi.org/10.1145/2576230

  • Chernyak E. An Approach to the Problem of Annotation of Research Publications. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. (429-434).

    https://doi.org/10.1145/2684822.2697032

  • Zhu M, Xiong W and Wu Y. Learning to Rank with Only Positive Examples. Proceedings of the 2014 13th International Conference on Machine Learning and Applications. (87-92).

    https://doi.org/10.1109/ICMLA.2014.19

  • Li H. (2014). Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition. Synthesis Lectures on Human Language Technologies. 10.2200/S00607ED2V01Y201410HLT026. 7:3. (1-121). Online publication date: 2-Oct-2014.

    http://www.morganclaypool.com/doi/abs/10.2200/S00607ED2V01Y201410HLT026

  • Bahadori M, Chang Y, Long B and Liu Y. Scalable heterogeneous transfer ranking. Proceedings of the 3rd International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications - Volume 36. (214-228).

    /doi/10.5555/2999973.2999989

  • Yu A and Grauman K. Fine-Grained Visual Comparisons with Local Learning. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. (192-199).

    https://doi.org/10.1109/CVPR.2014.32

  • Gao W and Yang P. Democracy is good for ranking. Proceedings of the 7th ACM international conference on Web search and data mining. (63-72).

    https://doi.org/10.1145/2556195.2556267

  • Zhu M and Wu Y. Search by multiple examples. Proceedings of the 7th ACM international conference on Web search and data mining. (667-672).

    https://doi.org/10.1145/2556195.2556206

  • Zhang X, He B, Luo T, Li D and Xu J. Clustering-based transduction for learning a ranking model with limited human labels. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. (1777-1782).

    https://doi.org/10.1145/2505515.2505647

  • Zhang X, He B and Luo T. Learn to Rank Tweets by Integrating Query-Specific Characteristics. International Workshop on Behavior and Social Informatics on Behavior and Social Computing - Volume 8178. (224-236).

    https://doi.org/10.1007/978-3-319-04048-6_20

  • Dammak F, Kammoun H and Hamadou A. (2013). A Semi-Supervised and Active Learning Method for Alternatives Ranking Functions. Distributed Networks. 10.1201/b15282-8. (151-164). Online publication date: 2-Aug-2013.

    http://www.crcnetbase.com/doi/abs/10.1201/b15282-8

  • Wang H, He X, Chang M, Song Y, White R and Chu W. Personalized ranking model adaptation for web search. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. (323-332).

    https://doi.org/10.1145/2484028.2484068

  • Lin Y, Lin H, Xu K and Sun X. (2013). Learning to rank using smoothing methods for language modeling. Journal of the American Society for Information Science and Technology. 10.1002/asi.22789. 64:4. (818-828). Online publication date: 1-Apr-2013.

    https://onlinelibrary.wiley.com/doi/10.1002/asi.22789

  • Pan Z, You X, Chen H, Tao D and Pang B. (2013). Generalization performance of magnitude-preserving semi-supervised ranking with graph-based regularization. Information Sciences: an International Journal. 221. (284-296). Online publication date: 1-Feb-2013.

    https://doi.org/10.1016/j.ins.2012.09.003

  • Wang B, Tang J, Fan W, Chen S, Tan C and Yang Z. (2013). Query-dependent cross-domain ranking in heterogeneous network. Knowledge and Information Systems. 34:1. (109-145). Online publication date: 1-Jan-2013.

    https://doi.org/10.1007/s10115-011-0472-7

  • Cheng F, Zhang X, He B, Luo T and Wang W. A survey of learning to rank for real-time twitter search. Proceedings of the 2012 international conference on Pervasive Computing and the Networked World. (150-164).

    https://doi.org/10.1007/978-3-642-37015-1_13

  • Zhang X, He B, Luo T and Li B. Query-biased learning to rank for real-time twitter search. Proceedings of the 21st ACM international conference on Information and knowledge management. (1915-1919).

    https://doi.org/10.1145/2396761.2398543

  • Nappi M and Wechsler H. (2012). Robust re-identification using randomness and statistical learning. Pattern Recognition Letters. 33:14. (1820-1827). Online publication date: 1-Oct-2012.

    https://doi.org/10.1016/j.patrec.2012.02.005

  • Song S and Myaeng S. (2012). A novel term weighting scheme based on discrimination power obtained from past retrieval results. Information Processing and Management: an International Journal. 48:5. (919-930). Online publication date: 1-Sep-2012.

    https://doi.org/10.1016/j.ipm.2012.03.004

  • (2012). Learnable Ranking Models for Automatic Text Summarization and Information Retrieval. Textual Information Access. 10.1002/9781118562796.ch2. (33-58). Online publication date: 7-May-2012.

    https://onlinelibrary.wiley.com/doi/10.1002/9781118562796.ch2

  • Zhou K, Bai J, Zha H and Xue G. (2012). Leveraging Auxiliary Data for Learning to Rank. ACM Transactions on Intelligent Systems and Technology. 3:2. (1-21). Online publication date: 1-Feb-2012.

    https://doi.org/10.1145/2089094.2089113

  • Chang Y, Bai J, Zhou K, Xue G, Zha H and Zheng Z. (2012). Multi-task learning to rank for web search. Pattern Recognition Letters. 33:2. (173-181). Online publication date: 1-Jan-2012.

    https://doi.org/10.1016/j.patrec.2011.09.020

  • He H. (2011). A Co-Ranking Algorithm for Learning Listwise Ranking Functions from Unlabeled Data. Journal of Computers. 10.4304/jcp.6.11.2302-2309. 6:11.

    http://ojs.academypublisher.com/index.php/jcp/article/view/4613

  • Szummer M and Yilmaz E. Semi-supervised learning to rank with preference regularization. Proceedings of the 20th ACM international conference on Information and knowledge management. (269-278).

    https://doi.org/10.1145/2063576.2063620

  • Asadi N, Metzler D, Elsayed T and Lin J. Pseudo test collections for learning web search ranking functions. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. (1073-1082).

    https://doi.org/10.1145/2009916.2010058

  • Li H. (2011). Learning to Rank for Information Retrieval and Natural Language Processing. Synthesis Lectures on Human Language Technologies. 10.2200/S00348ED1V01Y201104HLT012. 4:1. (1-113). Online publication date: 22-Apr-2011.

    http://www.morganclaypool.com/doi/abs/10.2200/S00348ED1V01Y201104HLT012

  • Zhang J, Jing P, Ji Z and Su Y. (2011). Image Search Reranking with Transductive Learning to Rank Framework. Information Computing and Applications. 10.1007/978-3-642-27452-7_72. (529-536).

    http://link.springer.com/10.1007/978-3-642-27452-7_72

  • Liu T. (2011). Semi-supervised Ranking. Learning to Rank for Information Retrieval. 10.1007/978-3-642-14267-3_8. (123-126).

    http://link.springer.com/10.1007/978-3-642-14267-3_8

  • Bai J, Diaz F, Chang Y, Zheng Z and Chen K. Cross-market model adaptation with pairwise preference data for web search ranking. Proceedings of the 23rd International Conference on Computational Linguistics: Posters. (18-26).

    /doi/10.5555/1944566.1944569

  • Qin T, Liu T and Li H. (2009). A general approximation framework for direct optimization of information retrieval measures. Information Retrieval. 10.1007/s10791-009-9124-x. 13:4. (375-397). Online publication date: 1-Aug-2010.

    http://link.springer.com/10.1007/s10791-009-9124-x

  • Gao W, Cai P, Wong K and Zhou A. Learning to rank only using training data from related domain. Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. (162-169).

    https://doi.org/10.1145/1835449.1835478

  • Wechsler H. (2010). Intelligent Biometric Information Management. Intelligent Information Management. 10.4236/iim.2010.29060. 02:09. (499-511).

    http://www.scirp.org/journal/doi.aspx?DOI=10.4236/iim.2010.29060

  • Wang B, Tang J, Fan W, Chen S, Yang Z and Liu Y. Heterogeneous cross domain ranking in latent space. Proceedings of the 18th ACM conference on Information and knowledge management. (987-996).

    https://doi.org/10.1145/1645953.1646079

  • Lin Y, Lin H, Yang Z and Su S. A Boosting Approach for Learning to Rank Using SVD with Partially Labeled Data. Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology. (330-338).

    https://doi.org/10.1007/978-3-642-04769-5_29

  • Li M, Li H and Zhou Z. (2009). Semi-supervised document retrieval. Information Processing and Management: an International Journal. 45:3. (341-355). Online publication date: 1-May-2009.

    https://doi.org/10.1016/j.ipm.2008.11.002

  • Kim K and Choi S. Incremental learning to rank with partially-labeled data. Proceedings of the 2009 workshop on Web Search Click Data. (20-27).

    https://doi.org/10.1145/1507509.1507513