{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:27:28Z","timestamp":1743049648057,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031243820"},{"type":"electronic","value":"9783031243837"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-24383-7_23","type":"book-chapter","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T16:18:49Z","timestamp":1674577129000},"page":"415-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-gated FM: Revisiting the\u00a0Weight of\u00a0Feature Interactions for\u00a0CTR Prediction"],"prefix":"10.1007","author":[{"given":"Zhongxue","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiji","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Brauwers, G., Frasincar, F.: A general survey on attention mechanisms in deep learning. IEEE Trans. Knowl. Data Eng. 1 (2021)","DOI":"10.1109\/TKDE.2021.3126456"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, H., 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":"23_CR3","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1724\u20131734. ACL (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"23_CR4","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, pp. 933\u2013941. PMLR (2017)"},{"issue":"3","key":"23_CR5","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s41019-021-00159-z","volume":"6","author":"G Du","year":"2021","unstructured":"Du, G., Zhou, L., Yang, Y., L\u00fc, K., Wang, L.: Deep multiple auto-encoder-based multi-view clustering. Data Sci. Eng. 6(3), 323\u2013338 (2021)","journal-title":"Data Sci. Eng."},{"issue":"10","key":"23_CR6","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10), 2222\u20132232 (2017)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"23_CR7","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: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1725\u20131731 (2017). ijcai.org","DOI":"10.24963\/ijcai.2017\/239"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"He, X., Chua, T.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 355\u2013364. ACM (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Hong, F., Huang, D., Chen, G.: Interaction-aware factorization machines for recommender systems. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pp. 3804\u20133811. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33013804"},{"issue":"8","key":"23_CR10","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011\u20132023 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Huang, T., She, Q., Wang, Z., Zhang, J.: GateNet: gating-enhanced deep network for click-through rate prediction. CoRR abs\/2007.03519 (2020)","DOI":"10.5121\/csit.2020.101916"},{"key":"23_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: Proceedings of the 13th ACM Conference on Recommender Systems, 2019, pp. 169\u2013177. ACM (2019)","DOI":"10.1145\/3298689.3347043"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W., Lin, C.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems. pp. 43\u201350. ACM (2016)","DOI":"10.1145\/2959100.2959134"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Lan, L., Geng, Y.: Accurate and interpretable factorization machines. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pp. 4139\u20134146. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33014139"},{"key":"23_CR15","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, KDD, pp. 1754\u20131763. ACM (2018)","DOI":"10.1145\/3219819.3220023"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Liu, B., et al.: AutoFIS: automatic feature interaction selection in factorization models for click-through rate prediction. In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2636\u20132645. ACM (2020)","DOI":"10.1145\/3394486.3403314"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Luo, Y., et al.: AutoCross: automatic feature crossing for tabular data in real-world applications. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 1936\u20131945. ACM (2019)","DOI":"10.1145\/3292500.3330679"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 1412\u20131421. The Association for Computational Linguistics (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Pan, J., et al.: Field-weighted factorization machines for click-through rate prediction in display advertising. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, pp. 1349\u20131357. ACM (2018)","DOI":"10.1145\/3178876.3186040"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: ICDM 2010, The 10th IEEE International Conference on Data Mining, pp. 995\u20131000. IEEE Computer Society (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"23_CR21","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, CIKM, pp. 1161\u20131170. ACM (2019)","DOI":"10.1145\/3357384.3357925"},{"issue":"1","key":"23_CR22","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Sun, Y., Pan, J., Zhang, A., Flores, A.: FM$$^2$$: field-matrixed factorization machines for recommender systems. In: WWW 2021: The Web Conference 2021, pp. 2828\u20132837. ACM\/IW3C2 (2021)","DOI":"10.1145\/3442381.3449930"},{"key":"23_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"23_CR25","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. 12:1\u201312:7. ACM (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.: Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, pp. 3119\u20133125 (2017). ijcai.org","DOI":"10.24963\/ijcai.2017\/435"},{"key":"23_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-30671-1_4","volume-title":"Advances in Information Retrieval","author":"W Zhang","year":"2016","unstructured":"Zhang, W., Du, T., Wang, J.: Deep learning over multi-field categorical data. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 45\u201357. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-30671-1_4"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD, pp. 1059\u20131068. ACM (2018)","DOI":"10.1145\/3219819.3219823"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, J., Liu, J., Yang, S., Zhang, Q., He, X.: FuxiCTR: an open benchmark for click-through rate prediction. arXiv preprint arXiv:2009.05794 (2020)","DOI":"10.1145\/3459637.3482486"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24383-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T16:26:39Z","timestamp":1674577599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24383-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031243820","9783031243837"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24383-7_23","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/collaboratecom.eai-conferences.org\/2022","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":"Confyplus.eai.eu","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"171","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":"57","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":"33% - 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":"4","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)"}}]}}