{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:30:38Z","timestamp":1792787438517,"version":"4.5.0"},"reference-count":80,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272001, 62206002"],"award-info":[{"award-number":["62272001, 62206002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["2208085QF195"],"award-info":[{"award-number":["2208085QF195"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hefei Key Common Technology Project","award":["GJ2022GX15"],"award-info":[{"award-number":["GJ2022GX15"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Click-through rate (CTR) prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervision and communication signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address these issues, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network (CETN). Drawing inspiration from sociology, CETN leverages the complementary nature of diversity and homogeneity to guide the model in acquiring higher-quality feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss.<\/jats:p>","DOI":"10.1145\/3688571","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:10:03Z","timestamp":1723461003000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6818-7834","authenticated-orcid":false,"given":"Honghao","family":"Li","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1480-6522","authenticated-orcid":false,"given":"Lei","family":"Sang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-0668","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7353-4159","authenticated-orcid":false,"given":"Xuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macquarie University, Macquarie Park, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8709-1088","authenticated-orcid":false,"given":"Yiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1145\/2872427.2883033","volume-title":"Proceedings of the 25th International Conference on World Wide Web","author":"Borisov Alexey","year":"2016","unstructured":"Alexey Borisov, Ilya Markov, Maarten De Rijke, and Pavel Serdyukov. 2016. A neural click model for web search. In Proceedings of the 25th International Conference on World Wide Web, 531\u2013541."},{"key":"e_1_3_2_3_2","volume-title":"Diversity and Homogeneity in World Societies","author":"Bourguignon Erika","year":"1973","unstructured":"Erika Bourguignon and Lenora Greenbaum. 1973. Diversity and Homogeneity in World Societies. HRAF Press, New Haven, CT."},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1145\/3459637.3481915","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Chen Bo","year":"2021","unstructured":"Bo Chen, Yichao Wang, Zhirong Liu, Ruiming Tang, Wei Guo, Hongkun Zheng, Weiwei Yao, Muyu Zhang, and Xiuqiang He. 2021. Enhancing explicit and implicit feature interactions via information sharing for parallel deep CTR models. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3757\u20133766."},{"key":"e_1_3_2_5_2","first-page":"1597","volume-title":"International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_6_2","first-page":"108","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Chen Xu","year":"2018","unstructured":"Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 108\u2013116."},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2988450.2988454","volume-title":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems","author":"Cheng Heng-Tze","year":"2016","unstructured":"Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 7\u201310."},{"key":"e_1_3_2_8_2","first-page":"3609","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Cheng Weiyu","year":"2020","unstructured":"Weiyu Cheng, Yanyan Shen, and Linpeng Huang. 2020. Adaptive factorization network: Learning adaptive-order feature interactions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 3609\u20133616."},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1145\/2959100.2959190","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"Covington Paul","year":"2016","unstructured":"Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, 191\u2013198."},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1145\/3437963.3441727","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Deng Wei","year":"2021","unstructured":"Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, and Guang Lin. 2021. Deeplight: Deep lightweight feature interactions for accelerating CTR predictions in ad serving. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 922\u2013930."},{"key":"e_1_3_2_11_2","unstructured":"Zhenhua Dong Zhe Wang Jun Xu Ruiming Tang and Jirong Wen. 2022. A brief history of recommender systems. arXiv:2209.01860. Retrieved from https:\/\/arxiv.org\/abs\/2209.01860"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing Stefan","year":"2018","unstructured":"Stefan Elfwing, Eiji Uchibe, and Kenji Doya. 2018. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107 (2018), 3\u201311.","journal-title":"Neural Networks"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Yufei Feng Fuyu Lv Weichen Shen Menghan Wang Fei Sun Yu Zhu and Keping Yang. 2019. Deep session interest network for click-through rate prediction. Retrieved from https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3367243.3367359","DOI":"10.24963\/ijcai.2019\/319"},{"key":"e_1_3_2_14_2","first-page":"355","volume-title":"Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Gao Jianfeng","year":"2009","unstructured":"Jianfeng Gao, Wei Yuan, Xiao Li, Kefeng Deng, and Jian-Yun Nie. 2009. Smoothing clickthrough data for web search ranking. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 355\u2013362."},{"issue":"3","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446662","article-title":"User response prediction in online advertising","volume":"54","author":"Gharibshah Zhabiz","year":"2021","unstructured":"Zhabiz Gharibshah and Xingquan Zhu. 2021. User response prediction in online advertising. ACM Computing Surveys (CSUR) 54, 3 (2021), 1\u201343.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_16_2","unstructured":"Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. Retrieved from https:\/\/openreview.net\/forum?id=S1v4N2l0-"},{"key":"e_1_3_2_17_2","first-page":"1725","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI \u201917)","author":"Guo Huifeng","year":"2017","unstructured":"Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI \u201917). AAAI Press, 1725\u20131731."},{"key":"e_1_3_2_18_2","first-page":"727","volume-title":"Proceedings of the IEEE 38th International Conference on Data Engineering (ICDE)","author":"Guo Wei","year":"2022","unstructured":"Wei Guo, Can Zhang, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, and Rui Zhang. 2022. Miss: Multi-interest self-supervised learning framework for click-through rate prediction. In Proceedings of the IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 727\u2013740."},{"key":"e_1_3_2_19_2","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778."},{"key":"e_1_3_2_20_2","first-page":"355","volume-title":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 355\u2013364."},{"key":"e_1_3_2_21_2","first-page":"639","volume-title":"Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 639\u2013648."},{"key":"e_1_3_2_22_2","first-page":"10086","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"H\u00e9naff Olivier J","year":"2021","unstructured":"Olivier J H\u00e9naff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron Van den Oord, Oriol Vinyals, and Joao Carreira. 2021. Efficient visual pretraining with contrastive detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 10086\u201310096."},{"key":"e_1_3_2_23_2","unstructured":"Huawei. 2021. An Open-Source CTR Prediction Library. Retrieved from https:\/\/fuxictr.github.io"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627158"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1145\/2959100.2959134","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"Juan Yuchin","year":"2016","unstructured":"Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems, 43\u201350."},{"key":"e_1_3_2_26_2","first-page":"1","volume-title":"Proceedings of the 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","author":"Kaloev Martin","year":"2021","unstructured":"Martin Kaloev and Georgi Krastev. 2021. Comparative analysis of activation functions used in the hidden layers of deep neural networks. In Proceedings of the 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 1\u20135."},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1145\/3340531.3411912","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Khawar Farhan","year":"2020","unstructured":"Farhan Khawar, Xu Hang, Ruiming Tang, Bin Liu, Zhenguo Li, and Xiuqiang He. 2020. Autofeature: Searching for feature interactions and their architectures for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 625\u2013634."},{"key":"e_1_3_2_28_2","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"3","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1080\/09589236.2013.833086","article-title":"Discursive managerial practices of diversity and homogeneity","volume":"24","author":"Knoppers Annelies","year":"2015","unstructured":"Annelies Knoppers, Inge Claringbould, and Marianne Dortants. 2015. Discursive managerial practices of diversity and homogeneity. Journal of Gender Studies 24, 3 (2015), 259\u2013274.","journal-title":"Journal of Gender Studies"},{"key":"e_1_3_2_30_2","first-page":"96","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Lai Riwei","year":"2023","unstructured":"Riwei Lai, Li Chen, Yuhan Zhao, Rui Chen, and Qilong Han. 2023. Disentangled negative sampling for collaborative filtering. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, 96\u2013104."},{"key":"e_1_3_2_31_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv:1909.11942. Retrieved from https:\/\/arxiv.org\/abs\/1909.11942"},{"key":"e_1_3_2_32_2","first-page":"539","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Li Zekun","year":"2019","unstructured":"Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. 2019. FiGNN: Modeling feature interactions via graph neural networks for ctr prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 539\u2013548."},{"key":"e_1_3_2_33_2","unstructured":"Zekun Li Shu Wu Zeyu Cui and Xiaoyu Zhang. 2022. GraphFM: Graph factorization machines for feature interaction modeling. arXiv:2105.11866. Retrieved from https:\/\/arxiv.org\/abs\/2105.11866"},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1145\/3219819.3220023","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Lian Jianxun","year":"2018","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 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1754\u20131763."},{"key":"e_1_3_2_35_2","first-page":"1384","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Lin Jianghao","year":"2023","unstructured":"Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, and Weinan Zhang. 2023. MAP: A model-agnostic pretraining framework for click-through rate prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1384\u20131395."},{"key":"e_1_3_2_36_2","first-page":"2320","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Lin Zihan","year":"2022","unstructured":"Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In Proceedings of the ACM Web Conference 2022, 2320\u20132329."},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1037\/10286-008","volume-title":"Attribution and Social Interaction: The Legacy of Edward E. Jones","author":"Linville Patricia W.","year":"1998","unstructured":"Patricia W. Linville. 1998. The heterogeneity of homogeneity. In Attribution and Social Interaction: The Legacy of Edward E. Jones. J. M. Darley and J. Cooper (Eds.), American Psychological Association, 423\u2013487."},{"key":"e_1_3_2_38_2","first-page":"2636","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Liu Bin","year":"2020","unstructured":"Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. 2020. 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, 2636\u20132645."},{"key":"e_1_3_2_39_2","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv:1907.11692. Retrieved from https:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_2_40_2","first-page":"4552","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Mao Kelong","year":"2023","unstructured":"Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, and Zhenhua Dong. 2023. FinalMLP: An enhanced two-stream MLP model for CTR prediction. Proceedings of the AAAI Conference on Artificial Intelligence 37, 4 (2023), 4552\u20134560."},{"key":"e_1_3_2_41_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748. Retrieved from https:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_2_42_2","first-page":"1349","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Pan Junwei","year":"2018","unstructured":"Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, and Quan Lu. 2018. Field-weighted factorization machines for click-through rate prediction in display advertising. In Proceedings of the 2018 World Wide Web Conference, 1349\u20131357."},{"key":"e_1_3_2_43_2","unstructured":"Yujie Pan Jiangchao Yao Bo Han Kunyang Jia Ya Zhang and Hongxia Yang. 2021. Click-through rate prediction with auto-quantized contrastive learning. arXiv:2109.13921. Retrieved from https:\/\/arxiv.org\/abs\/2109.13921"},{"key":"e_1_3_2_44_2","first-page":"8026","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, 8026\u2013803."},{"key":"e_1_3_2_45_2","first-page":"1","volume-title":"Affect and Groups","author":"Phillips Katherine W.","year":"2007","unstructured":"Katherine W. Phillips and Robert B. Lount. 2007. The affective consequences of diversity and homogeneity in groups. In Affect and Groups, Vol. 10, Emerald Group Publishing Limited, 1\u201320."},{"key":"e_1_3_2_46_2","first-page":"579","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Qiu Ruihong","year":"2019","unstructured":"Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 579\u2013588."},{"key":"e_1_3_2_47_2","first-page":"1149","volume-title":"Proceedings of the IEEE 16th International Conference on Data Mining (ICDM)","author":"Qu Yanru","year":"2016","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 Proceedings of the IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1149\u20131154."},{"issue":"1","key":"e_1_3_2_48_2","first-page":"1","article-title":"Product-based neural networks for user response prediction over multi-field categorical data","volume":"37","author":"Qu Yanru","year":"2018","unstructured":"Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. 2018. Product-based neural networks for user response prediction over multi-field categorical data. ACM Transactions on Information Systems 37, 1 (2018), 1\u201335.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_49_2","first-page":"1137","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Ren Xubin","year":"2023","unstructured":"Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, and Chao Huang. 2023. Disentangled contrastive collaborative filtering. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1137\u20131146."},{"key":"e_1_3_2_50_2","first-page":"995","volume-title":"Proceedings of the IEEE International Conference on Data Mining","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle. 2010. Factorization machines. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 995\u20131000."},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1145\/3383313.3412488","volume-title":"Proceedings of the 14th ACM Conference on Recommender Systems","author":"Rendle Steffen","year":"2020","unstructured":"Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In Proceedings of the 14th ACM Conference on Recommender Systems, 240\u2013248."},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1145\/1242572.1242643","volume-title":"Proceedings of the 16th International Conference on World Wide Web","author":"Richardson Matthew","year":"2007","unstructured":"Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the 16th International Conference on World Wide Web, 521\u2013530."},{"key":"e_1_3_2_53_2","first-page":"3067","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Shalev-Shwartz Shai","year":"2017","unstructured":"Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah. 2017. Failures of gradient-based deep learning. In Proceedings of the International Conference on Machine Learning. PMLR, 3067\u20133075."},{"issue":"3","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564283","article-title":"RESUS: Warm-up cold users via meta-learning residual user preferences in CTR prediction","volume":"41","author":"Shen Yanyan","year":"2023","unstructured":"Yanyan Shen, Lifan Zhao, Weiyu Cheng, Zibin Zhang, Wenwen Zhou, and Lin Kangyi. 2023. RESUS: Warm-up cold users via meta-learning residual user preferences in CTR prediction. ACM Transactions on Information Systems 41, 3 (2023), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1145\/3357384.3357925","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Song Weiping","year":"2019","unstructured":"Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. AutoInt: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1161\u20131170."},{"key":"e_1_3_2_56_2","first-page":"1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Szegedy Christian","year":"2015","unstructured":"Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1\u20139."},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1145\/3539618.3591681","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Tian Zhen","year":"2023","unstructured":"Zhen Tian, Ting Bai, Wayne Xin Zhao, Ji-Rong Wen, and Zhao Cao. 2023. EulerNet: Adaptive feature interaction learning via Euler\u2019s formula for CTR prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1376\u20131385."},{"issue":"1","key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3522673","article-title":"Sequential recommendation with multiple contrast signals","volume":"41","author":"Wang Chenyang","year":"2023","unstructured":"Chenyang Wang, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. Sequential recommendation with multiple contrast signals. ACM Transactions on Information Systems 41, 1 (2023), 1\u201327.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1145\/3583780.3615089","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","author":"Wang Fangye","year":"2023","unstructured":"Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, and Ning Gu. 2023. Towards deeper, lighter and interpretable cross network for CTR prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2523\u20132533."},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1145\/3539597.3570372","volume-title":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","author":"Wang Fangye","year":"2023","unstructured":"Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2023. CL4CTR: A contrastive learning framework for CTR prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 805\u2013813."},{"key":"e_1_3_2_61_2","first-page":"1","volume-title":"Proceedings of the ADKDD\u201917","author":"Wang Ruoxi","year":"2017","unstructured":"Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD\u201917, 1\u20137."},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1145\/3442381.3450078","volume-title":"Proceedings of the Web Conference 2021","author":"Wang Ruoxi","year":"2021","unstructured":"Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021. DCNv2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the Web Conference 2021, 1785\u20131797."},{"key":"e_1_3_2_63_2","unstructured":"Zhiqiang Wang Qingyun She and Junlin Zhang. 2021. MaskNet: Introducing feature-wise multiplication to CTR ranking models by instance-guided mask. arXiv:2102.07619. Retrieved from https:\/\/arxiv.org\/abs\/2102.07619"},{"key":"e_1_3_2_64_2","first-page":"726","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Wu Jiancan","year":"2021","unstructured":"Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 726\u2013735."},{"key":"e_1_3_2_65_2","first-page":"70","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Xia Lianghao","year":"2022","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022. Hypergraph contrastive collaborative filtering. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 70\u201379."},{"key":"e_1_3_2_66_2","first-page":"3119","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","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. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, 3119\u20133125."},{"key":"e_1_3_2_67_2","unstructured":"Bing Xu Naiyan Wang Tianqi Chen and Mu Li. 2015. Empirical evaluation of rectified activations in convolutional network. arXiv:1505.00853. Retrieved from https:\/\/arxiv.org\/abs\/1505.00853"},{"issue":"2","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"102853","DOI":"10.1016\/j.ipm.2021.102853","article-title":"Click-through rate prediction in online advertising: A literature review","volume":"59","author":"Yang Yanwu","year":"2022","unstructured":"Yanwu Yang and Panyu Zhai. 2022. Click-through rate prediction in online advertising: A literature review. Information Processing & Management 59, 2 (2022), Article 102853.","journal-title":"Information Processing & Management"},{"key":"e_1_3_2_69_2","first-page":"4321","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Yao Tiansheng","year":"2021","unstructured":"Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, and Evan Ettinger. 2021. Self-supervised learning for large-scale item recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4321\u20134330."},{"issue":"2","key":"e_1_3_2_70_2","first-page":"1","article-title":"Joint modeling of user check-in behaviors for real-time point-of-interest recommendation","volume":"35","author":"Yin Hongzhi","year":"2016","unstructured":"Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia Sadiq. 2016. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems 35, 2 (2016), 1\u201344.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3288135"},{"key":"e_1_3_2_72_2","first-page":"1294","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yu Junliang","year":"2022","unstructured":"Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1294\u20131303."},{"key":"e_1_3_2_73_2","first-page":"436","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Yu Runlong","year":"2021","unstructured":"Runlong Yu, Yuyang Ye, Qi Liu, Zihan Wang, Chunfeng Yang, Yucheng Hu, and Enhong Chen. 2021. Xcrossnet: Feature structure-oriented learning for click-through rate prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 436\u2013447."},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3632402"},{"issue":"1","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1500000066","article-title":"Explainable recommendation: A survey and new perspectives","volume":"14","author":"Zhang Yongfeng","year":"2020","unstructured":"Yongfeng Zhang and Xu Chen. 2020. Explainable recommendation: A survey and new perspectives. Foundations and Trends\u00ae in Information Retrieval 14, 1 (2020), 1\u2013101.","journal-title":"Foundations and Trends\u00ae in Information Retrieval"},{"issue":"3","key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3573385","article-title":"Revisiting graph-based recommender systems from the perspective of variational auto-encoder","volume":"41","author":"Zhang Yi","year":"2023","unstructured":"Yi Zhang, Yiwen Zhang, Dengcheng Yan, Shuiguang Deng, and Yun Yang. 2023. Revisiting graph-based recommender systems from the perspective of variational auto-encoder. ACM Transactions on Information Systems 41, 3 (2023), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"2671","DOI":"10.1145\/3511808.3557479","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Zhang Zhao-Yu","year":"2022","unstructured":"Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang, Biye Jiang, Shuguang Han, Hongbo Deng, and Bo Zheng. 2022. Towards understanding the overfitting phenomenon of deep click-through rate models. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2671\u20132680."},{"key":"e_1_3_2_78_2","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1145\/3219819.3219823","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Zhou Guorui","year":"2018","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 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1059\u20131068."},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3134985"},{"key":"e_1_3_2_80_2","first-page":"2006","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhu Jieming","year":"2023","unstructured":"Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, and Rui Zhang. 2023b. FINAL: Factorized interaction layer for ctr prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006\u20132010."},{"key":"e_1_3_2_81_2","first-page":"2759","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Zhu Jieming","year":"2021","unstructured":"Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2021. Open benchmarking for click-through rate prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2759\u20132769."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688571","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:10:30Z","timestamp":1750281030000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3688571"],"URL":"https:\/\/doi.org\/10.1145\/3688571","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"2024-03-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}