{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T12:10:26Z","timestamp":1779019826772,"version":"3.51.4"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372253"],"award-info":[{"award-number":["62372253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Tianjin Natural Science Foundation Project","award":["23JCYBJC00010"],"award-info":[{"award-number":["23JCYBJC00010"]}]},{"name":"Nankai University School of Optometry and Vision Science Open Fund Program","award":["NKSGP202308"],"award-info":[{"award-number":["NKSGP202308"]}]},{"name":"Huawei Innovation Research Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Efficient and effective modeling of feature interactions is key to large-scale Click-Through Rate (CTR) prediction. Although existing feature interaction methods have improved the model accuracy, their computational consumption still increase exponentially with the number of feature fields and become severe efficiency bottleneck in real-world industrial scenarios. To address the issues, we propose an\n                    <jats:italic toggle=\"yes\">E<\/jats:italic>\n                    fficient and\n                    <jats:italic toggle=\"yes\">E<\/jats:italic>\n                    ffective\n                    <jats:italic toggle=\"yes\">NET<\/jats:italic>\n                    work for large-scale CTR prediction named\n                    <jats:italic toggle=\"yes\">EENet<\/jats:italic>\n                    . EENet presents a new alternating stacking architecture of implicit and explicit interaction layers, and each implicit layer in EENet can reduce both local computational and parameter load remarkably. EENet also designs a unified explicit interaction operation which can only use simple matrix multiplication to capture field-wise patterns. Moreover, the order of multiplications in EENet is rearranged to further decrease the computational complexity from quadratic to linear with respect to the number of feature fields. EENet thus can support the high efficiency in real-practice industrial scenarios with hundreds of feature fields. A set of extensive experiments is performed on two public datasets and one industrial dataset for effectiveness evaluation, and five larger-scale synthetic datasets for efficiency evaluation. The results highlight that our EENet can significantly outperform the state-of-the-art models in terms of both efficiency and scalability, while also maintaining superior effectiveness. Compared with DCNv2 and FiBiNet, EENet achieves 8.06\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and 36.72\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    efficiency improvements in training, and 2.02\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and 48.88\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    improvements in inference, respectively. Our solution and source code are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Yeedzhi\/EENet\">https:\/\/github.com\/Yeedzhi\/EENet<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3805800","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:05:20Z","timestamp":1775052320000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EENet: An Efficient and Effective Network for Large-Scale CTR Prediction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3553-734X","authenticated-orcid":false,"given":"Dezhi","family":"Yi","sequence":"first","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3750-2533","authenticated-orcid":false,"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-6394","authenticated-orcid":false,"given":"Ye","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science, DISSec, NDSTlab, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8681-7519","authenticated-orcid":false,"given":"Hang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8666-4142","authenticated-orcid":false,"given":"Suqi","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9165-4311","authenticated-orcid":false,"given":"Yangsen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-0221","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3375-6928","authenticated-orcid":false,"given":"Kenan","family":"Song","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7393-8994","authenticated-orcid":false,"given":"Huifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9031-9696","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Shanghai, 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 Technologies, 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 Technologies, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-Located with the 16th ACM Conference on Recommender Systems","author":"Anil Rohan","year":"2022","unstructured":"Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I. Shamir, et al. 2022. On the factory floor: ML engineering for industrial-scale ads recommendation models. In Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling co-Located with the 16th ACM Conference on Recommender Systems."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498435"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_7_2","unstructured":"Weiyu Cheng Yanyan Shen and Linpeng Huang. 2020. Differentiable neural input search for recommender systems. arXiv:2006.04466. Retrieved from https:\/\/arxiv.org\/abs\/2006.04466"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441727"},{"key":"e_1_3_2_9_2","first-page":"762","volume-title":"Proceedings of the 5th MLSys Conference","author":"Desai Aditya","year":"2022","unstructured":"Aditya Desai, Li Chou, and Anshumali Shrivastava. 2022. Random offset block embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. In Proceedings of the 5th MLSys Conference, 762\u2013778."},{"issue":"177","key":"e_1_3_2_10_2","first-page":"1","article-title":"All models are wrong, but many are useful: Learning a variable\u2019s 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\u2019s importance by studying an entire class of prediction models simultaneously. Journal of Machine Learning Research 20, 177 (2019), 1\u201381.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570365"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9517710"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467077"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615469"},{"key":"e_1_3_2_17_2","unstructured":"Yi Guo Zhaocheng Liu Jianchao Tan Chao Liao Sen Yang Lei Yuan Dongying Kong Zhi Chen and Ji Liu. 2022. LPFS: Learnable polarizing feature selection for click-through rate prediction. arXiv:2206.00267. Retrieved from https:\/\/arxiv.org\/abs\/2206.00267"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683898"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347043"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531762"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671571"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_2_24_2","first-page":"1","article-title":"CETN: Contrast-enhanced through network for click-through rate prediction","volume":"43","author":"Li Honghao","year":"2024","unstructured":"Honghao Li, Lei Sang, Yi Zhang, Xuyun Zhang, and Yiwen Zhang. 2024. CETN: Contrast-enhanced through network for click-through rate prediction. ACM Transactions on Information Systems 43, 1 (2024), 1\u201334.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681203"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25564"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645396"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539204"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401082"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403314"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679842"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403319"},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"Liu Siyi","year":"2021","unstructured":"Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, and Yong Li. 2021. Learnable embedding sizes for recommender systems. In Proceedings of the 9th International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=vQzcqQWIS0q"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583545"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25577"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532060"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0151"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511970"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403059"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591681"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608769"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570372"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450078"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591767"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512071"},{"key":"e_1_3_2_51_2","volume-title":"Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021","author":"Wang Zhiqiang","year":"2021","unstructured":"Zhiqiang Wang, Qingyun She, and Junlin Zhang. 2021. MaskNet: Introducing feature-wise multiplication to CTR ranking models by instance-guided mask. In Proceedings of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021. Retrieved from https:\/\/dlp-kdd.github.io\/assets\/pdf\/DLP-KDD_2021_paper_3.pdf"},{"key":"e_1_3_2_52_2","first-page":"1075","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Xi Yunjia","year":"2023","unstructured":"Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, and Yong Yu. 2023. A bird\u2019s-eye view of reranking: From list level to page level. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, 1075\u20131083."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457236"},{"key":"e_1_3_2_54_2","first-page":"24740","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922)","author":"Yan Bencheng","year":"2022","unstructured":"Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Hongbo Deng, Jian Xu, and Bo Zheng. 2022. APG: Adaptive parameter generation network for click-through rate prediction. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922), 24740\u201324752."},{"key":"e_1_3_2_55_2","unstructured":"Jie Amy Yang Jianyu Huang Jongsoo Park Ping Tak Peter Tang and Andrew Tulloch. 2020. Mixed-precision embedding using a cache. arXiv:2010.11305. Retrieved from https:\/\/arxiv.org\/abs\/2010.11305"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688136"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102853"},{"key":"e_1_3_2_58_2","first-page":"448","volume-title":"Proceedings of the 4th MLSys Conference","author":"Yin Chunxing","year":"2021","unstructured":"Chunxing Yin, Bilge Acun, Carole-Jean Wu, and Xing Liu. 2021. TT-Rec: Tensor train compression for deep learning recommendation models. In Proceedings of the 4th MLSys Conference, 448\u2013462."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75765-6_35"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539034"},{"key":"e_1_3_2_61_2","first-page":"15190","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922)","author":"Zha Daochen","year":"2022","unstructured":"Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, and Xia Hu. 2022. DreamShard: Generalizable embedding table placement for recommender systems. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS \u201922), 15190\u201315203."},{"key":"e_1_3_2_62_2","first-page":"59421","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Zhang Buyun","year":"2024","unstructured":"Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, et al. 2024. Wukong: Towards a scaling law for large-scale recommendation. In Proceedings of the 41st International Conference on Machine Learning. PMLR, 59421\u201359434. Retrieved from https:\/\/openreview.net\/forum?id=8iUgr2nuwo"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615499"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412227"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531723"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591988"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482486"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3805800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T11:44:53Z","timestamp":1779018293000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3805800"],"URL":"https:\/\/doi.org\/10.1145\/3805800","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]},"assertion":[{"value":"2025-04-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}