{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T01:48:34Z","timestamp":1775094514082,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"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":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498435","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"57-65","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":65,"title":["CAN"],"prefix":"10.1145","author":[{"given":"Weijie","family":"Bian","sequence":"first","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Kailun","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Lejian","family":"Ren","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Qi","family":"Pi","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Yujing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Can","family":"Xiao","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Xiang-Rong","family":"Sheng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Yong-Nan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Zhangming","family":"Chan","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Na","family":"Mou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Xinchen","family":"Luo","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Shiming","family":"Xiang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Guorui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Xiaoqiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]},{"given":"Hongbo","family":"Deng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Zhangming Chan Yuchi Zhang Xiuying Chen Shen Gao Zhiqiang Zhang Dongyan Zhao and Rui Yan. 2020. Selection and Generation: Learning towards Multi-Product Advertisement Post Generation. In EMNLP. 3818--3829.","DOI":"10.18653\/v1\/2020.emnlp-main.313"},{"key":"e_1_3_2_1_3_1","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 DLRS. 7--10.","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Yufei Feng Fuyu Lv Weichen Shen MenghanWang Fei Sun Yu Zhu and Keping Yang. 2019. Deep Session Interest Network for Click-Through Rate Prediction. In IJCAI. 2301--2307.","DOI":"10.24963\/ijcai.2019\/319"},{"key":"e_1_3_2_1_5_1","first-page":"729","article-title":"A new model for learning in graph domains","volume":"2","author":"Gori Marco","year":"2005","unstructured":"Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005. A new model for learning in graph domains. In IJCNN, Vol. 2. 729--734.","journal-title":"IJCNN"},{"key":"e_1_3_2_1_6_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. Deepfm: a factorization-machine based neural network for ctr prediction. In IJCAI. 2782--2788."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_8_1","volume-title":"GSN: A graph-structured network for multi-party dialogues. In IJCAI. 5010--5016.","author":"Hu Wenpeng","year":"2019","unstructured":"Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, and Rui Yan. 2019. GSN: A graph-structured network for multi-party dialogues. In IJCAI. 5010--5016."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Yuchin Juan Yong Zhuang Wei-Sheng Chin and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In RecSys. 43--50.","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_1_10_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_11_1","unstructured":"Chao Li Zhiyuan Liu Mengmeng Wu Yuchi Xu Huan Zhao Pipei Huang Guoliang Kang Qiwei Chen Wei Li and Dik Lun Lee. 2019. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In CIKM. 2615-- 2623."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Jianxun Lian Xiaohuan Zhou Fuzheng Zhang Zhongxia Chen Xing Xie and Guangzhong Sun. 2018. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In SIGKDD. 1754--1763.","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Qi Pi Weijie Bian Guorui Zhou Xiaoqiang Zhu and Kun Gai. 2019. Practice on Long Sequential User Behavior Modeling for Click-through Rate Prediction. In SIGKDD. 1059--1068.","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_1_14_1","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. In ICDM. 1149--1154."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3233770"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle. 2010. Factorization machines. In ICDM. 995--1000.","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Xiang-Rong Sheng Liqin Zhao Guorui Zhou Xinyao Ding Binding Dai Qiang Luo Siran Yang Jingshan Lv Chi Zhang Hongbo Deng and Xiaoqiang Zhu. 2021. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In CIKM. 4104--4113.","DOI":"10.1145\/3459637.3481941"},{"key":"e_1_3_2_1_18_1","first-page":"357","article-title":"Heterogeneous Information Network Embedding for Recommendation","volume":"31","author":"Shi Chuan","year":"2019","unstructured":"Chuan Shi, Binbin Hu, Wayne Xin Zhao, and Philip S. Yu. 2019. Heterogeneous Information Network Embedding for Recommendation. IEEE TKDE 31, 2 (2019), 357--370.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_1_21_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008."},{"key":"e_1_3_2_1_22_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_23_1","first-page":"1","article-title":"Deep & Cross Network for Ad Click Predictions","volume":"12","author":"Fu Bin","year":"2017","unstructured":"RuoxiWang, Bin Fu, Gang Fu, and MingliangWang. 2017. Deep & Cross Network for Ad Click Predictions. In ADKDD. 12:1--12:7.","journal-title":"ADKDD."},{"key":"e_1_3_2_1_24_1","volume-title":"Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617","author":"Xiao Jun","year":"2017","unstructured":"Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617 (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.020"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Huan Zhao Quanming Yao Jianda Li Yangqiu Song and Dik Lun Lee. 2017. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. In SIGKDD. 635--644.","DOI":"10.1145\/3097983.3098063"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Na Mou Ying Fan Qi Pi Weijie Bian Chang Zhou Xiaoqiang Zhu and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. In AAAI. 5941--5948.","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Xiaoqiang Zhu Chenru Song Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2018. Deep interest network for click-through rate prediction. In SIGKDD. 1059--1068.","DOI":"10.1145\/3219819.3219823"}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event AZ USA","acronym":"WSDM '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498435","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498435","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:51Z","timestamp":1750191531000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498435"}},"subtitle":["Feature Co-Action Network for Click-Through Rate Prediction"],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":28,"alternative-id":["10.1145\/3488560.3498435","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3498435","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}