{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:23:23Z","timestamp":1743031403222,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030309510"},{"type":"electronic","value":"9783030309527"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30952-7_4","type":"book-chapter","created":{"date-parts":[[2019,9,17]],"date-time":"2019-09-17T13:04:56Z","timestamp":1568725496000},"page":"28-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semi-supervised Learning to Rank with Uncertain Data"],"prefix":"10.1007","author":[{"given":"Xin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"ZhongQi","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"ChengHou","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,16]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1561\/1500000016","volume":"3","author":"T Liu","year":"2011","unstructured":"Liu, T.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3, 225\u2013331 (2011)","journal-title":"Found. Trends Inf. Retrieval"},{"doi-asserted-by":"crossref","unstructured":"Szummer, M., Yilmaz, E.: Semi-supervised learning to rank with preference regularization. In: Proceedings of the 20th ACM Conference on Conference on Information and Knowledge Management, CIKM 2011, pp. 269\u2013278 (2011)","key":"4_CR2","DOI":"10.1145\/2063576.2063620"},{"doi-asserted-by":"crossref","unstructured":"van den Akker, B., Markov, I., de Rijken, M.: ViTOR: learning to rank webpages based on visual features. In: The Web Conference (2019)","key":"4_CR3","DOI":"10.1145\/3308558.3313419"},{"unstructured":"Zoghi, M., Tunys, T., Ghavamzadeh, M., Kveton, B., Szepesvari, C., Wen, Z.: Online learning to rank in stochastic click models. In: Proceedings of the 20th ACM Conference on Conference on Proceedings of the 34th International Conference on Machine Learning, pp. 4199\u20134208 (2017)","key":"4_CR4"},{"doi-asserted-by":"crossref","unstructured":"Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: Proceedings of the 38th International ACM SIGIR Conference (2015)","key":"4_CR5","DOI":"10.1145\/2766462.2767738"},{"unstructured":"Wang, B., Klabjan, D.: An attention-based deep net for learning to rank (2017). arXiv preprint arXiv","key":"4_CR6"},{"unstructured":"Qin, T., Liu, T.: Introducing LETOR 4.0 datasets. Technical Report Microsoft Research Asia (2013)","key":"4_CR7"},{"doi-asserted-by":"crossref","unstructured":"Ganjisaffar, Y., Caruana, R., Lope, C.: Bagging gradient-boosted trees for high precision, low variance ranking models. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 85\u201394. ACM, New York, NY, USA (2011)","key":"4_CR8","DOI":"10.1145\/2009916.2009932"},{"doi-asserted-by":"crossref","unstructured":"Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 133\u2013142. ACM, New York, NY, USA (2002)","key":"4_CR9","DOI":"10.1145\/775047.775067"},{"issue":"4","key":"4_CR10","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s11280-013-0215-7","volume":"17","author":"H Hu","year":"2014","unstructured":"Hu, H., Sha, C., Wang, X., Zhou, A.: A unified framework for semi-supervised Pu learning. World Wide Web 17(4), 493\u2013510 (2014)","journal-title":"World Wide Web"},{"key":"4_CR11","volume-title":"Semi-Supervised Learning","author":"O Chapelle","year":"2010","unstructured":"Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning, 1st edn. MIT Press, Cambridge (2010)","edition":"1"},{"doi-asserted-by":"crossref","unstructured":"Sellamanickam, S., Garg, P., Selvaraj, S.K.: A pairwise ranking based approach to learning with positive and unlabeled examples. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 663\u2013672. ACM, New York, NY, USA (2011)","key":"4_CR12","DOI":"10.1145\/2063576.2063675"},{"issue":"2","key":"4_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ipm.2012.08.002","volume":"49","author":"JX Huang","year":"2013","unstructured":"Huang, J.X., Miao, J., He, B.: High performance query expansion using adaptive co-training. Inf. Process. Manage. 49(2), 441\u2013453 (2013). https:\/\/doi.org\/10.1016\/j.ipm.2012.08.002","journal-title":"Inf. Process. Manage."},{"unstructured":"Usunier, N., Truong, V., Amini, M.R., Gallinari, P., Curie, M.: Ranking with unlabeled data: a first study. In: Proceedings of NIPS Workshop (2005)","key":"4_CR14"},{"doi-asserted-by":"crossref","unstructured":"Zhang, L., Ma, B., He, J., Li, G., Huang, Q., Tian, Q.: Adaptively unified semi-supervised learning for cross-modal retrieval. In: Proceedings of the Twenty-Sixth International Joint Conference on Articial Intelligence(IJCAI-17) (2017)","key":"4_CR15","DOI":"10.24963\/ijcai.2017\/476"},{"doi-asserted-by":"crossref","unstructured":"Duh, K., Kirchhoff, K.: Learning to rank with partially-labeled data. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 251\u2013258. ACM, New York, NY, USA (2008)","key":"4_CR16","DOI":"10.1145\/1390334.1390379"},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ipm.2008.11.002","volume":"45","author":"M Li","year":"2009","unstructured":"Li, M., Li, H., Zhou, Z.H.: Semi-supervised document retrieval. Inf. Process. Manage. 45, 341\u2013355 (2009)","journal-title":"Inf. Process. Manage."},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.engappai.2019.02.014","volume":"81","author":"A Kim","year":"2019","unstructured":"Kim, A., Cho, S.-B.: An ensemble semi-supervised learning method for predicting defaults in social lending. Eng. Appl. Artif. Intell. 81, 193\u2013199 (2019)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"4_CR19","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1109\/69.591457","volume":"9","author":"TP Hong","year":"1997","unstructured":"Hong, T.P., Tseng, S.S.: A generalized version space learning algorithm for noisy and uncertain data. IEEE Trans. Knowl. Data Eng. 9(2), 336\u2013340 (1997)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cogsys.2017.05.006","volume":"45","author":"PK Rhee","year":"2017","unstructured":"Rhee, P.K., Erdenee, E., Kyun, S.D., Ahmed, M.U., Jin, S.: Active and semi-supervised learning for object detection with imperfect data. Cogn. Syst. Res. 45, 109\u2013123 (2017)","journal-title":"Cogn. Syst. Res."},{"key":"4_CR21","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1016\/j.neucom.2010.09.024","volume":"74","author":"P Dallaire","year":"2011","unstructured":"Dallaire, P., Besse, C., Chaib-draa, B.: An approximate inference with Gaussian process to latent functions from uncertain data. Neurocomputing 74, 1945\u20131955 (2011)","journal-title":"Neurocomputing"},{"key":"4_CR22","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ins.2012.05.023","volume":"213","author":"C Liang","year":"2012","unstructured":"Liang, C., Zhang, Y., Shi, P., Hu, Z.: Information sciences learning very fast decision tree from uncertain data streams with positive and unlabeled samples. Inf. Sci. 213, 50\u201367 (2012)","journal-title":"Inf. Sci."},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/S1007-0214(10)70112-7","volume":"15","author":"M Zhu","year":"2010","unstructured":"Zhu, M., Gao, Z., Qi, G., Ji, Q.: DLP learning from uncertain data. Tsinghua Sci. Technol. 15, 650\u2013656 (2010)","journal-title":"Tsinghua Sci. Technol."},{"doi-asserted-by":"crossref","unstructured":"Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2002, pp. 299\u2013306. ACM, New York, NY, USA (2002)","key":"4_CR24","DOI":"10.1145\/564376.564429"},{"doi-asserted-by":"crossref","unstructured":"Zhou, Y., Croft, W.B.: Query performance prediction in web search environments. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 543\u2013550. ACM, New York, NY, USA (2007)","key":"4_CR25","DOI":"10.1145\/1277741.1277835"},{"issue":"2","key":"4_CR26","doi-asserted-by":"publisher","first-page":"11:1","DOI":"10.1145\/2180868.2180873","volume":"30","author":"A Shtok","year":"2012","unstructured":"Shtok, A., Kurland, O., Carmel, D., Raiber, F., Markovits, G.: Predicting query performance by query-drift estimation. ACM Trans. Inf. Syst. 30(2), 11:1\u201311:35 (2012)","journal-title":"ACM Trans. Inf. Syst."},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ins.2014.09.009","volume":"293","author":"T Reitmaie","year":"2015","unstructured":"Reitmaie, T., Calma, A., Sick, B.: Transductive active learning \u2013a new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Inf. Sci. 293, 275\u2013298 (2015)","journal-title":"Inf. Sci."},{"issue":"1","key":"4_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-005-4258-6","volume":"58","author":"GI Webb","year":"2005","unstructured":"Webb, G.I., Boughton, J.R., Wang, Z.: Not so naive bayes: aggregating one-dependence estimators. Mach. Learn. 58(1), 5\u201324 (2005)","journal-title":"Mach. Learn."},{"issue":"1","key":"4_CR29","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ssci.2008.01.002","volume":"47","author":"SK Palei","year":"2009","unstructured":"Palei, S.K., Das, S.K.: Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf. Sci. 47(1), 88\u201396 (2009)","journal-title":"Saf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., He, B., Luo, T.: Transductive learning for real-time twitter search. In: The International Conference on Weblogs and Social Media (ICWSM), pp. 611\u2013614 (2012)","key":"4_CR30","DOI":"10.1609\/icwsm.v6i1.14285"},{"doi-asserted-by":"crossref","unstructured":"Liu, T., Xu, J., Qin, T., Xiong, W., Li, H.: LETOR: benchmark dataset for research on learning to rank for information retrieval. In: SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (2007)","key":"4_CR31","DOI":"10.1561\/9781601982452"},{"issue":"5","key":"4_CR32","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.ipm.2011.01.002","volume":"47","author":"X Geng","year":"2011","unstructured":"Geng, X., Qin, T., Liu, T., Cheng, X., Li, H.: Selecting optimal training data for learning to rank. Inf. Process. Manage. 47(5), 730\u2013741 (2011)","journal-title":"Inf. Process. Manage."},{"key":"4_CR33","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-014-0781-x","volume":"113","author":"Y Yang","year":"2015","unstructured":"Yang, Y., Ma, Z., Nie, F., Chang, X., Hauptmann, A.G.: Multi-class active learning by uncertainty sampling with diversity maximization. Int. J. Comput. Vis. 113, 113\u2013127 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"4_CR34","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.compchemeng.2017.07.004","volume":"106","author":"C Shang","year":"2017","unstructured":"Shang, C., Huang, X., You, F.: Data-driven robust optimization based on kernel learning. Comput. Chem. Eng. 106, 464\u2013479 (2017)","journal-title":"Comput. Chem. Eng."},{"key":"4_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-02934-0_3","volume-title":"Web Information Systems and Applications","author":"J Liu","year":"2018","unstructured":"Liu, J., Cui, R., Zhao, Y.: Multilingual short text classification via convolutional neural network. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 27\u201338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02934-0_3"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30952-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T16:15:17Z","timestamp":1721751317000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30952-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030309510","9783030309527"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30952-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa22019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/jisq.nju.edu.cn\/wisa2019\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"154","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}