{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:32:52Z","timestamp":1742938372909,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970216"},{"type":"electronic","value":"9789819970223"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-7022-3_3","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"29-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AutoShape: Automatic Design of Click-Through Rate Prediction Models Using Shapley Value"],"prefix":"10.1007","author":[{"given":"Yunfei","family":"Fang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-3661","authenticated-orcid":false,"given":"Caihong","family":"Mu","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhu, C., Li, G.: AutoFIS: Automatic feature interaction selection in factorization models for click-through rate prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2636\u20132645 (2020)","DOI":"10.1145\/3394486.3403314"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Zheng, R., Qu, L., Cui, B., et al.: AutoML for deep recommender systems: a survey. ACM Trans. Inform. Syst. (2023)","DOI":"10.1145\/3579355"},{"key":"3_CR3","unstructured":"Wan, X., Ru, B., Esperan\u00e7a, P. M.,\u00a0 Li, Z.: On redundancy and diversity in cell-based neural architecture search. arXiv preprint arXiv:2203.08887 (2022)"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Meng, Z., Zhang, J., Li, Y., et al.: A general method for automatic discovery of powerful interactions in click-through rate prediction. In: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1298\u20131307 (2021)","DOI":"10.1145\/3404835.3462842"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Zhu, G., Cheng, F., Lian, D., et al.: NAS-CTR: efficient neural architecture search for click-through rate prediction. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 332\u2013342 (2022)","DOI":"10.1145\/3477495.3532030"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Xiao, H., Wang, Z., Zhu, Z., et al.: Shapley-NAS: discovering operation contribution for neural architecture search. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11892\u201311901 (2022)","DOI":"10.1109\/CVPR52688.2022.01159"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: 2010 IEEE International conference on data mining, pp. 995\u20131000 (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Qu, Y., Cai, H., Ren, K.: Product-based neural networks for user response prediction. In: 16th international conference on data mining (ICDM), pp. 1149\u20131154 (2016)","DOI":"10.1109\/ICDM.2016.0151"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., et al.: Deep & cross network for ad click predictions. In: Proceedings of the ADKDD 2017, pp. 1\u20137 (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Lian, J., Zhou, X., Zhang, F., et al.: xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1754\u20131763 (2018)","DOI":"10.1145\/3219819.3220023"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"He, X.,\u00a0 Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355\u2013364 (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"3_CR12","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:13th ACM Conference on Recommender Systems, pp. 169\u2013177 (2019)","DOI":"10.1145\/3298689.3347043"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Song, Q., Cheng, D., Zhou, H.: Towards automated neural interaction discovery for click-through rate prediction. In: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 945\u2013955 (2020)","DOI":"10.1145\/3394486.3403137"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, W., Du, T., Wang, J.: Deep learning over multi-field categorical data: a case study on user response prediction. In: 38th\u00a0European Conference on IR Research, pp. 45\u201357. Springer International Publishing (2016).\u00a0https:\/\/doi.org\/10.1007\/978-3-319-30671-1_4","DOI":"10.1007\/978-3-319-30671-1_4"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Chen, B., Wang, Y., Liu, Z.: Enhancing explicit and implicit feature interactions via information sharing for parallel deep CTR models. In: 30th ACM international Conference on Information & Knowledge Management, pp. 3757\u20133766 (2021)","DOI":"10.1145\/3459637.3481915"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Cheng, H. T., Koc, L., Harmsen, J.: Wide & deep learning for recommender systems. In: 1st Workshop on Deep Learning for Recommender Systems, pp.7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)","DOI":"10.24963\/ijcai.2017\/239"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7022-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:08:57Z","timestamp":1699574937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7022-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970216","9789819970223"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7022-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","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":"422","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":"95","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":"36","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":"23% - 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":"3.1","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)"}}]}}