{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:16:38Z","timestamp":1743074198342,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306716"},{"type":"electronic","value":"9783031306723"}],"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\/978-3-031-30672-3_22","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T11:10:49Z","timestamp":1681384249000},"page":"330-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MOEF: Modeling Occasion Evolution in\u00a0Frequency Domain for\u00a0Promotion-Aware Click-Through Rate Prediction"],"prefix":"10.1007","author":[{"given":"Xiaofeng","family":"Pan","sequence":"first","affiliation":[]},{"given":"Yibin","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"He","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Chengjun","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"22_CR1","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. In: IJCAI (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"issue":"8","key":"22_CR2","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"1","key":"22_CR3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79\u201387 (1991)","journal-title":"Neural Comput."},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, C., Tong, B., Tan, J., Zeng, X., Zhuang, T.: Deep time-aware item evolution network for click-through rate prediction. In: CIKM, pp. 785\u2013794 (2020)","DOI":"10.1145\/3340531.3411952"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Liu, B., et al.: Autofis: automatic feature interaction selection in factorization models for click-through rate prediction. In: KDD, pp. 2636\u20132645 (2020)","DOI":"10.1145\/3394486.3403314"},{"key":"22_CR6","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Niu, X., Li, B., Li, C., Tan, J., Xiao, R., Deng, H.: Heterogeneous graph augmented multi-scenario sharing recommendation with tree-guided expert networks. In: WSDM, pp. 1038\u20131046 (2021)","DOI":"10.1145\/3437963.3441729"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Pan, X., et al.: Metacvr: conversion rate prediction via meta learning in small-scale recommendation scenarios. In: SIGIR, pp. 2110\u20132114 (2022)","DOI":"10.1145\/3477495.3531733"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Pi, Q., Bian, W., Zhou, G., Zhu, X., Gai, K.: Practice on long sequential user behavior modeling for click-through rate prediction. In: KDD, pp. 2671\u20132679 (2019)","DOI":"10.1145\/3292500.3330666"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Ren, K., et al.: Lifelong sequential modeling with personalized memorization for user response prediction. In: SIGIR, pp. 565\u2013574 (2019)","DOI":"10.1145\/3331184.3331230"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285\u2013295 (2001)","DOI":"10.1145\/371920.372071"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Shen, Q., Tao, W., Zhang, J., Wen, H., Chen, Z., Lu, Q.: Sar-net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios. In: CIKM, pp. 4094\u20134103 (2021)","DOI":"10.1145\/3459637.3481948"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Sheng, X.R., et al.: One model to serve all: star topology adaptive recommender for multi-domain ctr prediction. In: CIKM, pp. 4104\u20134113 (2021)","DOI":"10.1145\/3459637.3481941"},{"key":"22_CR14","unstructured":"Soliman, S.S., Srinath, M.D.: Continuous and discrete signals and systems. Englewood Cliffs (1990)"},{"key":"22_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998\u20136008 (2017)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Wang, J., Louca, R., Hu, D., Cellier, C., Caverlee, J., Hong, L.: Time to shop for valentine\u2019s day: shopping occasions and sequential recommendation in e-commerce. In: WSDM, pp. 645\u2013653 (2020)","DOI":"10.1145\/3336191.3371836"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Yang, L., Jiang, W., Wei, Y., Hu, Y., Wang, H.: Deep multi-interest network for click-through rate prediction. In: CIKM, pp. 2265\u20132268 (2020)","DOI":"10.1145\/3340531.3412092"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Yue, Z., et al.: Ts2vec: towards universal representation of time series. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i8.20881"},{"issue":"10","key":"22_CR19","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1109\/JIOT.2020.3039359","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Tao, D.: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 8(10), 7789\u20137817 (2020)","journal-title":"IEEE Internet Things J."},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: AAAI (2019)","DOI":"10.1145\/3219819.3219823"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: KDD (2018)","DOI":"10.1145\/3219819.3219823"},{"key":"22_CR22","unstructured":"Zhu, C., et al.: Aim: automatic interaction machine for click-through rate prediction. IEEE Trans. Knowl. Data Eng. (2021)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Zhuang, H., Wang, X., Bendersky, M., Najork, M.: Feature transformation for neural ranking models. In: SIGIR, pp. 1649\u20131652 (2020)","DOI":"10.1145\/3397271.3401333"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30672-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:27:24Z","timestamp":1710264444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30672-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306716","9783031306723"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30672-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","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":"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":"652","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":"125","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":"66","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":"19% - 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","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":"7.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}