{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T16:22:19Z","timestamp":1759940539501,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589335.3648300","type":"proceedings-article","created":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T18:41:21Z","timestamp":1715539281000},"page":"38-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["LightCS: Selecting Quadratic Feature Crosses in Linear Complexity"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1811-129X","authenticated-orcid":false,"given":"Zhaocheng","family":"Du","sequence":"first","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6279-7367","authenticated-orcid":false,"given":"Junhao","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3583-6719","authenticated-orcid":false,"given":"Qinglin","family":"Jia","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5730-8792","authenticated-orcid":false,"given":"Chuhan","family":"Wu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5666-8320","authenticated-orcid":false,"given":"Jieming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2231-4663","authenticated-orcid":false,"given":"Zhenhua","family":"Dong","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9224-2431","authenticated-orcid":false,"given":"Ruiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"unstructured":"[n. d.]. https:\/\/developers.google.com\/machine-learning\/crash-course\/featurecrosses\/ video-lecture","key":"e_1_3_2_2_1_1"},{"key":"e_1_3_2_2_2_1","volume-title":"The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? arXiv preprint arXiv:2010.05607","author":"Bastings Jasmijn","year":"2020","unstructured":"Jasmijn Bastings and Katja Filippova. 2020. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? arXiv preprint arXiv:2010.05607 (2020)."},{"key":"e_1_3_2_2_3_1","volume-title":"International Conference on Machine Learning. PMLR, 557--565","author":"Botev Aleksandar","year":"2017","unstructured":"Aleksandar Botev, Hippolyt Ritter, and David Barber. 2017. Practical gaussnewton optimisation for deep learning. In International Conference on Machine Learning. PMLR, 557--565."},{"key":"e_1_3_2_2_4_1","volume-title":"Dropout feature ranking for deep learning models. arXiv preprint arXiv:1712.08645","author":"Chang Chun-Hao","year":"2017","unstructured":"Chun-Hao Chang, Ladislav Rampasek, and Anna Goldenberg. 2017. Dropout feature ranking for deep learning models. arXiv preprint arXiv:1712.08645 (2017)."},{"doi-asserted-by":"crossref","unstructured":"Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir et al. 2016. Wide & deep learning for recommender systems. In dlprecsys. 7--10.","key":"e_1_3_2_2_5_1","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_6_1","volume-title":"Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. ArXiv abs\/1909.03276","author":"Cheng Weiyu","year":"2019","unstructured":"Weiyu Cheng, Yanyan Shen, and Linpeng Huang. 2019. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. ArXiv abs\/1909.03276 (2019). https:\/\/api.semanticscholar.org\/CorpusID:202539143"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_7_1","DOI":"10.1609\/aaai.v34i04.5768"},{"unstructured":"Gabriel Erion Joseph D Janizek Pascal Sturmfels Scott M Lundberg and Su-In Lee. 2019. Learning explainable models using attribution priors. (2019).","key":"e_1_3_2_2_8_1"},{"key":"e_1_3_2_2_9_1","first-page":"1","article-title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","volume":"20","author":"Fisher Aaron","year":"2019","unstructured":"Aaron Fisher, Cynthia Rudin, and Francesca Dominici. 2019. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. J. Mach. Learn. Res. 20, 177 (2019), 1--81.","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_10_1","DOI":"10.1016\/j.ecoinf.2019.101039"},{"unstructured":"Andrew Gibiansky. [n. d.]. Andrew Gibianskynbsp; nbsp;::nbsp; nbsp;mathnbsp;\u2192nbsp;[code]. https:\/\/andrew.gibiansky.com\/blog\/machinelearning\/gauss-newton-matrix\/","key":"e_1_3_2_2_11_1"},{"unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).","key":"e_1_3_2_2_12_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_13_1","DOI":"10.1145\/3583780.3615469"},{"key":"e_1_3_2_2_14_1","volume-title":"Multilayer feedforward networks are universal approximators. Neural networks 2, 5","author":"Hornik Kurt","year":"1989","unstructured":"Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2, 5 (1989), 359--366."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_15_1","DOI":"10.5555\/3546258.3546362"},{"key":"e_1_3_2_2_16_1","volume-title":"skinny neural networks are not universal approximators. arXiv preprint arXiv:1810.00393","author":"Johnson Jesse","year":"2018","unstructured":"Jesse Johnson. 2018. Deep, skinny neural networks are not universal approximators. arXiv preprint arXiv:1810.00393 (2018)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_17_1","DOI":"10.1145\/3534678.3539204"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_18_1","DOI":"10.1145\/3394486.3403314"},{"key":"e_1_3_2_2_19_1","volume-title":"Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation. In Companion Proceedings of the ACM Web Conference","author":"Liu Xiaofan","year":"2023","unstructured":"Xiaofan Liu, Qinglin Jia, Chuhan Wu, Jingjie Li, Dai Quanyu, Lin Bo, Rui Zhang, and Ruiming Tang. 2023. Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation. In Companion Proceedings of the ACM Web Conference 2023. 490--494."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_20_1","DOI":"10.1145\/3292500.3330679"},{"doi-asserted-by":"crossref","unstructured":"Fuyuan Lyu Xing Tang Huifeng Guo Ruiming Tang Xiuqiang He Rui Zhang and Xue Liu. 2022. Memorize factorize or be naive: Learning optimal feature interaction methods for CTR prediction. (2022) 1450--1462.","key":"e_1_3_2_2_21_1","DOI":"10.1109\/ICDE53745.2022.00113"},{"key":"e_1_3_2_2_22_1","volume-title":"Optimizing feature set for click-through rate prediction. arXiv preprint arXiv:2301.10909","author":"Lyu Fuyuan","year":"2023","unstructured":"Fuyuan Lyu, Xing Tang, Dugang Liu, Liang Chen, Xiuqiang He, and Xue Liu. 2023. Optimizing feature set for click-through rate prediction. arXiv preprint arXiv:2301.10909 (2023)."},{"key":"e_1_3_2_2_23_1","volume-title":"Higher-order interactions capture unexplained complexity in diverse communities. Nature ecology & evolution 1, 3","author":"Mayfield Margaret M","year":"2017","unstructured":"Margaret M Mayfield and Daniel B Stouffer. 2017. Higher-order interactions capture unexplained complexity in diverse communities. Nature ecology & evolution 1, 3 (2017), 0062."},{"key":"e_1_3_2_2_24_1","volume-title":"Local interpretable model-agnostic explanations for music content analysis. 53","author":"Mishra Saumitra","year":"2017","unstructured":"Saumitra Mishra, Bob L Sturm, and Simon Dixon. 2017. Local interpretable model-agnostic explanations for music content analysis. 53 (2017), 537--543."},{"unstructured":"Yanru Qu Han Cai Kan Ren Weinan Zhang Yong Yu Ying Wen and Jun Wang. 2016. Product-based neural networks for user response prediction. (2016) 1149--1154.","key":"e_1_3_2_2_25_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_26_1","DOI":"10.1109\/ICDM.2010.127"},{"doi-asserted-by":"crossref","unstructured":"Abe Sklar. 1996. Random variables distribution functions and copulas: a personal look backward and forward. Lecture notes-monograph series (1996) 1--14.","key":"e_1_3_2_2_27_1","DOI":"10.1214\/lnms\/1215452606"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_28_1","DOI":"10.1109\/TSC.2014.2365795"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Song Weiping","year":"2018","unstructured":"Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2018. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2018). https:\/\/api.semanticscholar. org\/CorpusID:53100214"},{"key":"e_1_3_2_2_30_1","volume-title":"Feature interaction interpretability: A case for explaining adrecommendation systems via neural interaction detection. arXiv preprint arXiv:2006.10966","author":"Tsang Michael","year":"2020","unstructured":"Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu. 2020. Feature interaction interpretability: A case for explaining adrecommendation systems via neural interaction detection. arXiv preprint arXiv:2006.10966 (2020)."},{"key":"e_1_3_2_2_31_1","volume-title":"Feature interaction interpretability: A case for explaining adrecommendation systems via neural interaction detection. arXiv preprint arXiv:2006.10966","author":"Tsang Michael","year":"2020","unstructured":"Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu. 2020. Feature interaction interpretability: A case for explaining adrecommendation systems via neural interaction detection. arXiv preprint arXiv:2006.10966 (2020)."},{"key":"e_1_3_2_2_32_1","volume-title":"How does this interaction affect me? interpretable attribution for feature interactions. Advances in neural information processing systems 33","author":"Tsang Michael","year":"2020","unstructured":"Michael Tsang, Sirisha Rambhatla, and Yan Liu. 2020. How does this interaction affect me? interpretable attribution for feature interactions. Advances in neural information processing systems 33 (2020), 6147--6159."},{"key":"e_1_3_2_2_33_1","volume-title":"1--7","author":"Wang Ruoxi","year":"2017","unstructured":"Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. (2017), 1--7."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_34_1","DOI":"10.1145\/3539618.3591767"},{"key":"e_1_3_2_2_35_1","volume-title":"Autofield: Automating feature selection in deep recommender systems.","author":"Wang Yejing","year":"2022","unstructured":"Yejing Wang, Xiangyu Zhao, Tong Xu, and XianWu. 2022. Autofield: Automating feature selection in deep recommender systems. (2022), 1977--1986."},{"doi-asserted-by":"crossref","unstructured":"Runlong Yu Xiang Xu Yuyang Ye Qi Liu and Enhong Chen. 2023. Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction. (2023) 3151--3161.","key":"e_1_3_2_2_36_1","DOI":"10.1145\/3580305.3599277"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_37_1","DOI":"10.1145\/3583780.3615499"},{"key":"e_1_3_2_2_38_1","volume-title":"Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52, 1","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52, 1 (2019), 1--38."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_39_1","DOI":"10.1145\/3442381.3450124"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_40_1","DOI":"10.1145\/3459637.3482486"}],"event":{"sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"acronym":"WWW '24","name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore"},"container-title":["Companion Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3648300","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589335.3648300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:36:51Z","timestamp":1755823011000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3648300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":40,"alternative-id":["10.1145\/3589335.3648300","10.1145\/3589335"],"URL":"https:\/\/doi.org\/10.1145\/3589335.3648300","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}