{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:05:19Z","timestamp":1775815519946,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434235","type":"print"},{"value":"9783031434242","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43424-2_26","type":"book-chapter","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T20:37:24Z","timestamp":1694983044000},"page":"426-442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Click-Aware Structure Transfer with\u00a0Sample Weight Assignment for\u00a0Post-Click Conversion Rate Estimation"],"prefix":"10.1007","author":[{"given":"Kai","family":"Ouyang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8227-8820","authenticated-orcid":false,"given":"Wenhao","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0499-8838","authenticated-orcid":false,"given":"Xuanji","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Hai-Tao","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"26_CR1","unstructured":"Chen, Y., Wu, L., Zaki, M.: Iterative deep graph learning for graph neural networks: Better and robust node embeddings. In: Advances in Neural Information Processing Systems, vol. 33, pp. 19314\u201319326 (2020)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Hadash, G., Shalom, O.S., Osadchy, R.: Rank and rate: multi-task learning for recommender systems. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 451\u2013454 (2018)","DOI":"10.1145\/3240323.3240406"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Huang, T., Zhang, Z., Zhang, J.: Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction. In: Proceedings of the 13th ACM Conference on Recommender Systems. pp. 169\u2013177 (2019)","DOI":"10.1145\/3298689.3347043"},{"issue":"3","key":"26_CR6","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2019","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535\u2013547 (2019)","journal-title":"IEEE Trans. Big Data"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W.S., Lin, C.J.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 43\u201350 (2016)","DOI":"10.1145\/2959100.2959134"},{"key":"26_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: xDeepFM: combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1754\u20131763 (2018)","DOI":"10.1145\/3219819.3220023"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhao, Z., Chen, J., Li, A., Hong, L., Chi, E.H.: SNR: sub-network routing for flexible parameter sharing in multi-task learning. In: AAAI, vol. 33, pp. 216\u2013223 (2019)","DOI":"10.1609\/aaai.v33i01.3301216"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1930\u20131939 (2018)","DOI":"10.1145\/3219819.3220007"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Ma, X., et al.: Entire space multi-task model: an effective approach for estimating post-click conversion rate. In: Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1137\u20131140 (2018)","DOI":"10.1145\/3209978.3210104"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Ni, Y., et al.: Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 596\u2013605 (2018)","DOI":"10.1145\/3219819.3219828"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Ouyang, K., et al.: Mining interest trends and adaptively assigning sample weight for session-based recommendation. arXiv preprint arXiv:2306.11610 (2023)","DOI":"10.1145\/3539618.3592021"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Ouyang, K., Xu, X., Tang, C., Chen, W., Zheng, H.: Social-aware sparse attention network for session-based social recommendation. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 2173\u20132183 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.159"},{"key":"26_CR16","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Qu, Y., et al.: Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1149\u20131154. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0151"},{"key":"26_CR18","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Song, W., et al.: AutoInt: automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1161\u20131170 (2019)","DOI":"10.1145\/3357384.3357925"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: Fourteenth ACM Conference on Recommender Systems, pp. 269\u2013278 (2020)","DOI":"10.1145\/3383313.3412236"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: ESCM2: entire space counterfactual multi-task model for post-click conversion rate estimation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363\u2013372 (2022)","DOI":"10.1145\/3477495.3531972"},{"key":"26_CR22","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, pp. 1\u20137 (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., Pei, J.: AM-GCN: adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1243\u20131253 (2020)","DOI":"10.1145\/3394486.3403177"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Wen, H., Zhang, J., Lin, Q., Yang, K., Huang, P.: Multi-level deep cascade trees for conversion rate prediction in recommendation system. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 338\u2013345 (2019)","DOI":"10.1609\/aaai.v33i01.3301338"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Wen, H., et al.: Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2377\u20132386 (2020)","DOI":"10.1145\/3397271.3401443"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Xi, D., et al.: Modeling the sequential dependence among audience multi-step conversions with multi-task learning in targeted display advertising. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3745\u20133755 (2021)","DOI":"10.1145\/3447548.3467071"},{"key":"26_CR27","unstructured":"Yang, H., Lu, Q., Qiu, A.X., Han, C.: Large scale CVR prediction through dynamic transfer learning of global and local features. In: Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 103\u2013119. PMLR (2016)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43424-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T20:41:08Z","timestamp":1694983268000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43424-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434235","9783031434242"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43424-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"As authors, we acknowledge the importance of maintaining the integrity of research and its presentation to avoid damaging the trust in the journal, the professionalism of scientific authorship, and ultimately the entire scientific endeavor. Therefore, we pledge to follow the rules of good scientific practice, which include:\u2013 <b>Manuscript Submission:<\/b> We will not submit the same manuscript to more than one journal simultaneously.\u2013 <b>Originality:<\/b> We will ensure that the submitted work is original and has not been published elsewhere, either partially or in full, in any form or language. We will provide transparency regarding the reuse of material to avoid concerns about self-plagiarism.\u2013 <b>Salami Slicing:<\/b> We will not split a single study into multiple parts to increase the quantity of submissions and submit them to various journals or to one journal over time.\u2013 <b>Concurrent Publication:<\/b> If we choose to publish concurrently or secondarily, we will meet certain conditions such as translations or manuscripts intended for a different group of readers.\u2013 <b>Data Presentation:<\/b> We will present our results clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. We will adhere to discipline-specific rules for acquiring, selecting, and processing data, and we will not present data, text, or theories by others as our own. Proper acknowledgments will be given for all materials, and we will secure permissions for copyrighted materials. We understand that the journal may use software to screen for plagiarism.\u2013 <b>Permissions:<\/b> We will obtain permissions for the use of software, questionnaires\/(web) surveys, and scales in our studies.\u2013 <b>Citation:<\/b> We will cite appropriate and relevant literature to support our claims in both research and non-research articles, and we will avoid excessive and inappropriate self-citation or coordinated efforts among several authors to collectively self-cite.\u2013 <b>Truthful Statements:<\/b> We will avoid making untrue statements or descriptions about an entity that could potentially be seen as personal attacks or allegations about that person.\u2013 <b>Public Health and National Security:<\/b> We will clearly identify research that may be misapplied to pose a threat to public health or national security.\u2013 <b>Authorship:<\/b> We will ensure that the author group, corresponding author, and order of authors are correct at submission.All of the above guidelines are essential for respecting third-party rights such as copyright and\/or moral rights. As authors, we recognize our responsibility to uphold the highest ethical standards in scientific research and publication.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}