{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:14:42Z","timestamp":1775229282525,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":128,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"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":[[2022,7,6]]},"DOI":"10.1145\/3477495.3531723","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:13Z","timestamp":1657206733000},"page":"2912-2923","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":82,"title":["BARS"],"prefix":"10.1145","author":[{"given":"Jieming","family":"Zhu","sequence":"first","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Quanyu","family":"Dai","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Liangcai","family":"Su","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}]},{"given":"Rong","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Jinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}]},{"given":"Guohao","family":"Cai","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Xi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"ruizhang.info, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2021. The AmazonBooks Dataset. https:\/\/jmcauley.ucsd.edu\/data\/amazon\/ amazonbooks  2021. The AmazonBooks Dataset. https:\/\/jmcauley.ucsd.edu\/data\/amazon\/ amazonbooks"},{"key":"e_1_3_2_2_2_1","unstructured":"2021. The Avazu Dataset. https:\/\/www.kaggle.com\/c\/avazu-ctr-prediction\/data  2021. The Avazu Dataset. https:\/\/www.kaggle.com\/c\/avazu-ctr-prediction\/data"},{"key":"e_1_3_2_2_3_1","unstructured":"2021. The Criteo Dataset. https:\/\/www.kaggle.com\/c\/criteo-display-adchallenge\/data  2021. The Criteo Dataset. https:\/\/www.kaggle.com\/c\/criteo-display-adchallenge\/data"},{"key":"e_1_3_2_2_4_1","unstructured":"2021. The Gowalla Dataset. https:\/\/snap.stanford.edu\/data\/loc-gowalla.html  2021. The Gowalla Dataset. https:\/\/snap.stanford.edu\/data\/loc-gowalla.html"},{"key":"e_1_3_2_2_5_1","unstructured":"2021. The KKBox Dataset. https:\/\/www.kaggle.com\/c\/kkbox-musicrecommendation-challenge  2021. The KKBox Dataset. https:\/\/www.kaggle.com\/c\/kkbox-musicrecommendation-challenge"},{"key":"e_1_3_2_2_6_1","unstructured":"2021. The Yelp Dataset. https:\/\/www.yelp.com\/dataset  2021. The Yelp Dataset. https:\/\/www.yelp.com\/dataset"},{"key":"e_1_3_2_2_7_1","unstructured":"2022. DeepCTR: Easy-to-use modular and extendible package of deep-learning based CTR models. https:\/\/github.com\/shenweichen\/deepctr  2022. DeepCTR: Easy-to-use modular and extendible package of deep-learning based CTR models. https:\/\/github.com\/shenweichen\/deepctr"},{"key":"e_1_3_2_2_8_1","unstructured":"2022. EasyRec: A tensorflow framework for large scale recommendation algorithms. https:\/\/github.com\/alibaba\/EasyRec  2022. EasyRec: A tensorflow framework for large scale recommendation algorithms. https:\/\/github.com\/alibaba\/EasyRec"},{"key":"e_1_3_2_2_9_1","unstructured":"2022. FuxiCTR: A configurable tunable and reproducible library for CTR prediction. https:\/\/github.com\/xue-pai\/FuxiCTR  2022. FuxiCTR: A configurable tunable and reproducible library for CTR prediction. https:\/\/github.com\/xue-pai\/FuxiCTR"},{"key":"e_1_3_2_2_10_1","unstructured":"2022. Milvus: Vector database built for scalable similarity search. https: \/\/milvus.io  2022. Milvus: Vector database built for scalable similarity search. https: \/\/milvus.io"},{"key":"e_1_3_2_2_11_1","unstructured":"2022. The MovieLens Dataset. https:\/\/grouplens.org\/datasets\/movielens  2022. The MovieLens Dataset. https:\/\/grouplens.org\/datasets\/movielens"},{"key":"e_1_3_2_2_12_1","unstructured":"2022. Open Science Policies. https:\/\/conf.researchr.org\/track\/icse-2022\/icse2022-open-science-policies  2022. Open Science Policies. https:\/\/conf.researchr.org\/track\/icse-2022\/icse2022-open-science-policies"},{"key":"e_1_3_2_2_13_1","unstructured":"2022. PaddleRec: A library of recommendation models based on PaddlePaddle. https:\/\/github.com\/PaddlePaddle\/PaddleRec  2022. PaddleRec: A library of recommendation models based on PaddlePaddle. https:\/\/github.com\/PaddlePaddle\/PaddleRec"},{"key":"e_1_3_2_2_14_1","unstructured":"2022. TensorFlow Recommenders: A library for building recommender system models using TensorFlow. https:\/\/github.com\/tensorflow\/recommenders  2022. TensorFlow Recommenders: A library for building recommender system models using TensorFlow. https:\/\/github.com\/tensorflow\/recommenders"},{"key":"e_1_3_2_2_15_1","unstructured":"2022. TorchRec: A Pytorch domain library for recommendation systems. https: \/\/github.com\/pytorch\/torchrec  2022. TorchRec: A Pytorch domain library for recommendation systems. https: \/\/github.com\/pytorch\/torchrec"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3475944"},{"key":"e_1_3_2_2_17_1","volume-title":"Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia.","author":"Anelli Vito Walter","year":"2021","unstructured":"Vito Walter Anelli , Alejandro Bellog\u00edn , Antonio Ferrara , Daniele Malitesta , Felice Antonio Merra , Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021 . Elliot : A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) . 2405--2414. Vito Walter Anelli, Alejandro Bellog\u00edn, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 2405--2414."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"e_1_3_2_2_19_1","volume-title":"Graph Convolutional Matrix Completion. In KDD'18 Deep Learning Day.","author":"van den Berg Rianne","year":"2018","unstructured":"Rianne van den Berg , Thomas N Kipf , and Max Welling . 2018 . Graph Convolutional Matrix Completion. In KDD'18 Deep Learning Day. Rianne van den Berg, Thomas N Kipf, and Max Welling. 2018. Graph Convolutional Matrix Completion. In KDD'18 Deep Learning Day."},{"key":"e_1_3_2_2_20_1","volume-title":"Higher-Order Factorization Machines. In Annual Conference on Neural Information Processing Systems (NeurIPS). 3351--3359","author":"Blondel Mathieu","year":"2016","unstructured":"Mathieu Blondel , Akinori Fujino , Naonori Ueda , and Masakazu Ishihata . 2016 . Higher-Order Factorization Machines. In Annual Conference on Neural Information Processing Systems (NeurIPS). 3351--3359 . Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. 2016. Higher-Order Factorization Machines. In Annual Conference on Neural Information Processing Systems (NeurIPS). 3351--3359."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/184656.180369"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531922"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3373807","article-title":"Efficient Neural Matrix Factorization without Sampling for Recommendation","volume":"38","author":"Chen Chong","year":"2020","unstructured":"Chong Chen , Min Zhang , Yongfeng Zhang , Yiqun Liu , and Shaoping Ma . 2020 . Efficient Neural Matrix Factorization without Sampling for Recommendation . ACM Transactions on Information Systems (TOIS) 38 , 2 (2020), 1 -- 28 . Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient Neural Matrix Factorization without Sampling for Recommendation. ACM Transactions on Information Systems (TOIS) 38, 2 (2020), 1--28.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_26_1","volume-title":"Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 3609--3616","author":"Cheng Weiyu","year":"2020","unstructured":"Weiyu Cheng , Yanyan Shen , and Linpeng Huang . 2020 . Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 3609--3616 . Weiyu Cheng, Yanyan Shen, and Linpeng Huang. 2020. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 3609--3616."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347058"},{"key":"e_1_3_2_2_29_1","volume-title":"The ACM International Conference on Information and Knowledge Management (CIKM). 355--363","author":"Dacrema Maurizio Ferrari","year":"2020","unstructured":"Maurizio Ferrari Dacrema , Federico Parroni , Paolo Cremonesi , and Dietmar Jannach . 2020 . Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems . In The ACM International Conference on Information and Knowledge Management (CIKM). 355--363 . Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, and Dietmar Jannach. 2020. Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. In The ACM International Conference on Information and Knowledge Management (CIKM). 355--363."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11865"},{"key":"e_1_3_2_2_31_1","volume-title":"Adversarial Training Methods for Network Embedding. In The World Wide Web Conference (WWW). 329--339","author":"Dai Quanyu","year":"2019","unstructured":"Quanyu Dai , Xiao Shen , Liang Zhang , Qiang Li , and Dan Wang . 2019 . Adversarial Training Methods for Network Embedding. In The World Wide Web Conference (WWW). 329--339 . Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, and Dan Wang. 2019. Adversarial Training Methods for Network Embedding. In The World Wide Web Conference (WWW). 329--339."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/309"},{"key":"e_1_3_2_2_34_1","volume-title":"Collaborative Memory Network for Recommendation Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR). 515--524","author":"Ebesu Travis","year":"2018","unstructured":"Travis Ebesu , Bin Shen , and Yi Fang . 2018 . Collaborative Memory Network for Recommendation Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR). 515--524 . Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative Memory Network for Recommendation Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR). 515--524."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/319"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML)","volume":"119","author":"Guo Ruiqi","year":"2020","unstructured":"Ruiqi Guo , Philip Sun , Erik Lindgren , Quan Geng , David Simcha , Felix Chern , and Sanjiv Kumar . 2020 . Accelerating Large-Scale Inference with Anisotropic Vector Quantization . In Proceedings of the 37th International Conference on Machine Learning (ICML) , Vol. 119 . 3887--3896. Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. In Proceedings of the 37th International Conference on Machine Learning (ICML), Vol. 119. 3887--3896."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052639"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347043"},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the 1st Workshop on the Impact of Recommender Systems.","author":"Jannach Dietmar","unstructured":"Dietmar Jannach , Oren Sar Shalom , and Joseph A. Konstan . 2019. Towards More Impactful Recommender Systems Research . In Proceedings of the 1st Workshop on the Impact of Recommender Systems. Dietmar Jannach, Oren Sar Shalom, and Joseph A. Konstan. 2019. Towards More Impactful Recommender Systems Research. In Proceedings of the 1st Workshop on the Impact of Recommender Systems."},{"key":"e_1_3_2_2_45_1","volume-title":"Billion-Scale Similarity Search with GPUs. arXiv:1702.08734","author":"Johnson Jeff","year":"2017","unstructured":"Jeff Johnson , Matthijs Douze , and Herv\u00e9 J\u00e9gou . 2017. Billion-Scale Similarity Search with GPUs. arXiv:1702.08734 ( 2017 ). Jeff Johnson, Matthijs Douze, and Herv\u00e9 J\u00e9gou. 2017. Billion-Scale Similarity Search with GPUs. arXiv:1702.08734 (2017)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys). 23--28","author":"Joseph","unstructured":"Joseph A. Konstan and Gediminas Adomavicius. 2013. Toward Identification and Adoption of Best Practices in Algorithmic Recommender Systems Research . In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys). 23--28 . Joseph A. Konstan and Gediminas Adomavicius. 2013. Toward Identification and Adoption of Best Practices in Algorithmic Recommender Systems Research. In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys). 23--28."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403226"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401078"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402729"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00981"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371785"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357951"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_2_57_1","volume-title":"The World Wide Web Conference, (WWW). 1119-- 1129","author":"Liu Bin","year":"2019","unstructured":"Bin Liu , Ruiming Tang , Yingzhi Chen , Jinkai Yu , Huifeng Guo , and Yuzhou Zhang . 2019 . Feature Generation by Convolutional Neural Network for ClickThrough Rate Prediction . In The World Wide Web Conference, (WWW). 1119-- 1129 . Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. 2019. Feature Generation by Convolutional Neural Network for ClickThrough Rate Prediction. In The World Wide Web Conference, (WWW). 1119-- 1129."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806603"},{"key":"e_1_3_2_2_59_1","volume-title":"AUC: a Misleading Measure of the Performance of Predictive Distribution Models. Global ecology and Biogeography 17, 2","author":"Lobo Jorge M","year":"2008","unstructured":"Jorge M Lobo , Alberto Jim\u00e9nez-Valverde , and Raimundo Real . 2008. AUC: a Misleading Measure of the Performance of Predictive Distribution Models. Global ecology and Biogeography 17, 2 ( 2008 ), 145--151. Jorge M Lobo, Alberto Jim\u00e9nez-Valverde, and Raimundo Real. 2008. AUC: a Misleading Measure of the Performance of Predictive Distribution Models. Global ecology and Biogeography 17, 2 (2008), 145--151."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347041"},{"key":"e_1_3_2_2_61_1","volume-title":"Disentangled Graph Convolutional Networks. In International Conference on Machine Learning (ICML). 4212--4221","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma , Peng Cui , Kun Kuang , Xin Wang , and Wenwu Zhu . 2019 . Disentangled Graph Convolutional Networks. In International Conference on Machine Learning (ICML). 4212--4221 . Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. In International Conference on Machine Learning (ICML). 4212--4221."},{"key":"e_1_3_2_2_62_1","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 1930--1939","author":"Ma Jiaqi","unstructured":"Jiaqi Ma , Zhe Zhao , Xinyang Yi , Jilin Chen , Lichan Hong , and Ed H. Chi . 2018. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixtureof-Experts . In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 1930--1939 . Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H. Chi. 2018. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixtureof-Experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 1930--1939."},{"key":"e_1_3_2_2_63_1","unstructured":"Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019. Learning Disentangled Representations for Recommendation. In Advances in Neural Information Processing Systems (NeurIPS). 5711--5722.  Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019. Learning Disentangled Representations for Recommendation. In Advances in Neural Information Processing Systems (NeurIPS). 5711--5722."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210104"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133036"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482297"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482291"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488200"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412728"},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186040"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_2_73_1","volume-title":"The 29th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2685--2692.","author":"Pi Qi","unstructured":"Qi Pi , Guorui Zhou , Yujing Zhang , Zhe Wang , Lejian Ren , Ying Fan , Xiaoqiang Zhu , and Kun Gai . 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction . In The 29th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2685--2692. Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In The 29th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2685--2692."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401440"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0151"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331230"},{"key":"e_1_3_2_2_77_1","volume-title":"Factorization Machines. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 995--1000","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle . 2010 . Factorization Machines. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 995--1000 . Steffen Rendle. 2010. Factorization Machines. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM). 995--1000."},{"key":"e_1_3_2_2_78_1","volume-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452--461","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars SchmidtThieme . 2009 . BPR: Bayesian Personalized Ranking from Implicit Feedback . In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452--461 . Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars SchmidtThieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452--461."},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412488"},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242643"},{"key":"e_1_3_2_2_81_1","volume-title":"Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (SIGIR). 1845--1848","author":"Sachdeva Noveen","unstructured":"Noveen Sachdeva and Julian J . McAuley. 2020. How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements . In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (SIGIR). 1845--1848 . Noveen Sachdeva and Julian J. McAuley. 2020. How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (SIGIR). 1845--1848."},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645746"},{"key":"e_1_3_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_2_84_1","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 255--262","author":"Shan Ying","unstructured":"Ying Shan , T. Ryan Hoens , Jian Jiao , Haijing Wang , Dong Yu , and J. C. Mao . 2016. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features . In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 255--262 . Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and J. C. Mao. 2016. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 255--262."},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2577348"},{"key":"e_1_3_2_2_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482264"},{"key":"e_1_3_2_2_87_1","volume-title":"RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. In The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM). 528--536","author":"Shenbin Ilya","unstructured":"Ilya Shenbin , Anton Alekseev , Elena Tutubalina , Valentin Malykh , and Sergey I. Nikolenko . 2020 . RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. In The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM). 528--536 . Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko. 2020. RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. In The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM). 528--536."},{"key":"e_1_3_2_2_88_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481941"},{"key":"e_1_3_2_2_89_1","volume-title":"NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation. arXiv preprint arXiv:2010.12256","author":"Song Jinbo","year":"2020","unstructured":"Jinbo Song , Chao Chang , Fei Sun , Xinbo Song , and Peng Jiang . 2020. NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation. arXiv preprint arXiv:2010.12256 ( 2020 ). Jinbo Song, Chao Chang, Fei Sun, Xinbo Song, and Peng Jiang. 2020. NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation. arXiv preprint arXiv:2010.12256 (2020)."},{"key":"e_1_3_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_2_91_1","volume-title":"Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW). 3251--3257","author":"Steck Harald","year":"2019","unstructured":"Harald Steck . 2019 . Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW). 3251--3257 . Harald Steck. 2019. Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW). 3251--3257."},{"key":"e_1_3_2_2_92_1","volume-title":"Khoshgoftaar","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M . Khoshgoftaar . 2009 . A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell . 2009 (2009), 421425:1--421425:19. Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. 2009 (2009), 421425:1--421425:19."},{"key":"e_1_3_2_2_93_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_2_94_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401123"},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449930"},{"key":"e_1_3_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412489"},{"key":"e_1_3_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_98_1","volume-title":"Holographic Factorization Machines for Recommendation. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5143--5150","author":"Tay Yi","year":"2019","unstructured":"Yi Tay , Shuai Zhang , Anh Tuan Luu , Siu Cheung Hui , Lina Yao , and Tran Dang Quang Vinh . 2019 . Holographic Factorization Machines for Recommendation. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5143--5150 . Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, and Tran Dang Quang Vinh. 2019. Holographic Factorization Machines for Recommendation. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5143--5150."},{"key":"e_1_3_2_2_99_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations (ICLR).","author":"Petar","year":"2018","unstructured":"Petar Veli?kovi?, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. In International Conference on Learning Representations (ICLR). Petar Veli?kovi?, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_100_1","volume-title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In 7th International Conference on Learning Representations (ICLR).","author":"Wang Alex","unstructured":"Alex Wang , Amanpreet Singh , Julian Michael , Felix Hill , Omer Levy , and Samuel R. Bowman . 2019 . GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In 7th International Conference on Learning Representations (ICLR). Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In 7th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_2_102_1","volume-title":"Cross-Batch Negative Sampling for Training Two-Tower Recommenders. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1632--1636","author":"Wang Jinpeng","year":"2021","unstructured":"Jinpeng Wang , Jieming Zhu , and Xiuqiang He . 2021 . Cross-Batch Negative Sampling for Training Two-Tower Recommenders. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1632--1636 . Jinpeng Wang, Jieming Zhu, and Xiuqiang He. 2021. Cross-Batch Negative Sampling for Training Two-Tower Recommenders. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1632--1636."},{"key":"e_1_3_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"e_1_3_2_2_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450078"},{"key":"e_1_3_2_2_105_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_106_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_2_107_1","volume-title":"Proceedings of the 26th Annual Conference on Learning Theory (COLT)","volume":"8","author":"Wang Yining","year":"2013","unstructured":"Yining Wang , Liwei Wang , Yuanzhi Li , Di He , Wei Chen , and Tie-Yan Liu . 2013 . A Theoretical Analysis of NDCG Ranking Measures . In Proceedings of the 26th Annual Conference on Learning Theory (COLT) , Vol. 8 . 6. Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. 2013. A Theoretical Analysis of NDCG Ranking Measures. In Proceedings of the 26th Annual Conference on Learning Theory (COLT), Vol. 8. 6."},{"key":"e_1_3_2_2_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357936"},{"key":"e_1_3_2_2_109_1","unstructured":"Bin Wu Zhongchuan Sun Xiangnan He Xiang Wang and Jonathan Staniforth. 2020. NeuRec: An Open Source Neural Recommender Library. https:\/\/github. com\/wubinzzu\/NeuRec  Bin Wu Zhongchuan Sun Xiangnan He Xiang Wang and Jonathan Staniforth. 2020. NeuRec: An Open Source Neural Recommender Library. https:\/\/github. com\/wubinzzu\/NeuRec"},{"key":"e_1_3_2_2_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_2_111_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_2_2_112_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/435"},{"key":"e_1_3_2_2_113_1","volume-title":"Learning Feature Interactions with Lorentzian Factorization Machine. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 6470--6477","author":"Xu Canran","year":"2020","unstructured":"Canran Xu and Ming Wu . 2020 . Learning Feature Interactions with Lorentzian Factorization Machine. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 6470--6477 . Canran Xu and Ming Wu. 2020. Learning Feature Interactions with Lorentzian Factorization Machine. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI). 6470--6477."},{"key":"e_1_3_2_2_114_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475514"},{"key":"e_1_3_2_2_115_1","volume-title":"Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference (WWW). 441--447","author":"Yang Ji","year":"2020","unstructured":"Ji Yang , Xinyang Yi , Derek Zhiyuan Cheng , Lichan Hong , Yang Li , Simon Xiaoming Wang , Taibai Xu , and Ed H Chi . 2020 . Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference (WWW). 441--447 . Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, and Ed H Chi. 2020. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference (WWW). 441--447."},{"key":"e_1_3_2_2_116_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_2_2_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331340"},{"key":"e_1_3_2_2_118_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.020"},{"key":"e_1_3_2_2_119_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_120_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412077"},{"key":"e_1_3_2_2_121_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/963"},{"key":"e_1_3_2_2_122_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/636"},{"key":"e_1_3_2_2_123_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482016"},{"key":"e_1_3_2_2_124_1","volume-title":"CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU. CoRR abs\/2204.06240","author":"Zheng Zangwei","year":"2022","unstructured":"Zangwei Zheng , Pengtai Xu , Xuan Zou , Da Tang , Zhen Li , Chenguang Xi , Peng Wu , Leqi Zou , Yijie Zhu , Ming Chen , Xiangzhuo Ding , Fuzhao Xue , Ziheng Qing , Youlong Cheng , and Yang You . 2022. CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU. CoRR abs\/2204.06240 ( 2022 ). Zangwei Zheng, Pengtai Xu, Xuan Zou, Da Tang, Zhen Li, Chenguang Xi, Peng Wu, Leqi Zou, Yijie Zhu, Ming Chen, Xiangzhuo Ding, Fuzhao Xue, Ziheng Qing, Youlong Cheng, and Yang You. 2022. CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU. CoRR abs\/2204.06240 (2022)."},{"key":"e_1_3_2_2_125_1","volume-title":"Deep Interest Evolution Network for Click-Through Rate Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5941--5948","author":"Zhou Guorui","year":"2019","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 The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5941--5948 . 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 The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). 5941--5948."},{"key":"e_1_3_2_2_126_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_2_127_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412704"},{"key":"e_1_3_2_2_128_1","volume-title":"Open Benchmarking for Click-Through Rate Prediction. In The 30th ACM International Conference on Information and Knowledge Management (CIKM). 2759--2769","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 The 30th ACM International Conference on Information and Knowledge Management (CIKM). 2759--2769 . Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2021. Open Benchmarking for Click-Through Rate Prediction. In The 30th ACM International Conference on Information and Knowledge Management (CIKM). 2759--2769."}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531723","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3531723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:07Z","timestamp":1750186927000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531723"}},"subtitle":["Towards Open Benchmarking for Recommender Systems"],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":128,"alternative-id":["10.1145\/3477495.3531723","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3531723","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}