{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:23:07Z","timestamp":1745986987117,"version":"3.40.4"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030461324","type":"print"},{"value":"9783030461331","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46133-1_27","type":"book-chapter","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:03:30Z","timestamp":1745964210000},"page":"451-466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scalable Bid Landscape Forecasting in Real-Time Bidding"],"prefix":"10.1007","author":[{"given":"Aritra","family":"Ghosh","sequence":"first","affiliation":[]},{"given":"Saayan","family":"Mitra","sequence":"additional","affiliation":[]},{"given":"Somdeb","family":"Sarkhel","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Viswanathan","family":"Swaminathan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"27_CR1","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265\u2013283 (2016)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Agarwal, D., Ghosh, S., Wei, K., You, S.: Budget pacing for targeted online advertisements at linkedin. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1613\u20131619. ACM (2014)","DOI":"10.1145\/2623330.2623366"},{"issue":"4","key":"27_CR3","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1287\/mnsc.2014.2022","volume":"61","author":"SR Balseiro","year":"2015","unstructured":"Balseiro, S.R., Besbes, O., Weintraub, G.Y.: Repeated auctions with budgets in Ad exchanges: approximations and design. Manage. Sci. 61(4), 864\u2013884 (2015)","journal-title":"Manage. Sci."},{"key":"27_CR4","unstructured":"Bishop, C.M.: Mixture density networks. Technical report. Citeseer (1994)"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Zhang, R., Li, W., Mao, J.: Bid landscape forecasting in online Ad exchange marketplace. In: KDD, pp. 265\u2013273. ACM (2011)","DOI":"10.1145\/2020408.2020454"},{"issue":"1","key":"27_CR6","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1257\/aer.97.1.242","volume":"97","author":"B Edelman","year":"2007","unstructured":"Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second-price auction: selling billions of dollars worth of keywords. Am. Econ. Rev. 97(1), 242\u2013259 (2007)","journal-title":"Am. Econ. Rev."},{"key":"27_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3626-0","volume-title":"Vector Quantization and Signal Compression","author":"A Gersho","year":"2012","unstructured":"Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression, vol. 159. Springer, Boston (2012). https:\/\/doi.org\/10.1007\/978-1-4615-3626-0"},{"issue":"2","key":"27_CR8","doi-asserted-by":"publisher","first-page":"505","DOI":"10.2307\/1913323","volume":"49","author":"WH Greene","year":"1981","unstructured":"Greene, W.H.: On the asymptotic bias of the ordinary least squares estimator of the Tobit model. Econometrica J. Econometric Soc. 49(2), 505\u2013513 (1981)","journal-title":"Econometrica J. Econometric Soc."},{"issue":"2","key":"27_CR9","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1214\/aos\/1176346507","volume":"12","author":"IR James","year":"1984","unstructured":"James, I.R., Smith, P.: Consistency results for linear regression with censored data. Ann. Stat. 12(2), 590\u2013600 (1984)","journal-title":"Ann. Stat."},{"issue":"282","key":"27_CR10","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","volume":"53","author":"EL Kaplan","year":"1958","unstructured":"Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457\u2013481 (1958)","journal-title":"J. Am. Stat. Assoc."},{"key":"27_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Lang, K.J., Moseley, B., Vassilvitskii, S.: Handling forecast errors while bidding for display advertising. In: Proceedings of the 21st International Conference on World Wide Web, pp. 371\u2013380. ACM (2012)","DOI":"10.1145\/2187836.2187887"},{"issue":"3","key":"27_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0304-4076(84)90004-6","volume":"25","author":"JL Powell","year":"1984","unstructured":"Powell, J.L.: Least absolute deviations estimation for the censored regression model. J. Econometrics 25(3), 303\u2013325 (1984)","journal-title":"J. Econometrics"},{"key":"27_CR14","unstructured":"Salimans, T., Karpathy, A., Chen, X., Kingma, D.P.: PixelCNN++: improving the PixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517 (2017)"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, W., Yuan, S.: Display advertising with real-time bidding (RTB) and behavioural targeting. arXiv preprint arXiv:1610.03013 (2016)","DOI":"10.1561\/9781680833119"},{"key":"27_CR16","unstructured":"Wang, P., Li, Y., Reddy, C.K.: Machine learning for survival analysis: a survey. arXiv preprint arXiv:1708.04649 (2017)"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for Ad click predictions. In: Proceedings of the ADKDD 2017, p. 12. ACM (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"27_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-319-46128-1_8","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Y Wang","year":"2016","unstructured":"Wang, Y., Ren, K., Zhang, W., Wang, J., Yu, Y.: Functional bid landscape forecasting for display advertising. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 115\u2013131. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46128-1_8"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Wu, W., Yeh, M.Y., Chen, M.S.: Deep censored learning of the winning price in the real time bidding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2526\u20132535. ACM (2018)","DOI":"10.1145\/3219819.3220066"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Wu, W.C.H., Yeh, M.Y., Chen, M.S.: Predicting winning price in real time bidding with censored data. In: KDD, pp. 1305\u20131314. ACM (2015)","DOI":"10.1145\/2783258.2783276"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Yuan, S., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: Proceedings of the Seventh International Workshop on Data Mining for Online Advertising, p. 3. ACM (2013)","DOI":"10.1145\/2501040.2501980"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Zen, H., Senior, A.: Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3844\u20133848. IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6854321"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1077\u20131086. ACM (2014)","DOI":"10.1145\/2623330.2623633"},{"key":"27_CR24","unstructured":"Zhang, W., Yuan, S., Wang, J., Shen, X.: Real-time bidding benchmarking with iPinYou dataset. arXiv preprint arXiv:1407.7073 (2014)"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhou, T., Wang, J., Xu, J.: Bid-aware gradient descent for unbiased learning with censored data in display advertising. In: KDD, pp. 665\u2013674. ACM (2016)","DOI":"10.1145\/2939672.2939713"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Zhu, W.Y., Shih, W.Y., Lee, Y.H., Peng, W.C., Huang, J.L.: A gamma-based regression for winning price estimation in real-time bidding advertising. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1610\u20131619. IEEE (2017)","DOI":"10.1109\/BigData.2017.8258095"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46133-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:03:41Z","timestamp":1745964221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46133-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461324","9783030461331"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46133-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"0","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":"18% - 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":"3.04","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":"5.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}