{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:21:59Z","timestamp":1785543719365,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Agriculture and Food Research Initiative (AFRI)","award":["2020-67021-32799"],"award-info":[{"award-number":["2020-67021-32799"]}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["1024178"],"award-info":[{"award-number":["1024178"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1947203;IIS-2002540"],"award-info":[{"award-number":["IIS-1947203;IIS-2002540"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467234","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:33:08Z","timestamp":1628879588000},"page":"2409-2419","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["PURE"],"prefix":"10.1145","author":[{"given":"Yao","family":"Zhou","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianpeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeinab","family":"Taghavi","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evren","family":"Korpeoglu","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kannan","family":"Achan","sequence":"additional","affiliation":[{"name":"Walmart Labs, Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingrui","family":"He","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Yikun Ban and Jingrui He. 2021. Local Clustering in Contextual Multi-Armed Bandits. In WWW. Yikun Ban and Jingrui He. 2021. Local Clustering in Contextual Multi-Armed Bandits. In WWW.","DOI":"10.1145\/3442381.3450058"},{"key":"e_1_3_2_2_2_1","volume-title":"Learning From Positive and Unlabeled Data: A Survey. CoRR","author":"Bekker Jessa","year":"2018","unstructured":"Jessa Bekker and Jesse Davis . 2018. Learning From Positive and Unlabeled Data: A Survey. CoRR ( 2018 ). arXiv:1811.04820 http:\/\/arxiv.org\/abs\/1811.04820 Jessa Bekker and Jesse Davis. 2018. Learning From Positive and Unlabeled Data: A Survey. CoRR (2018). arXiv:1811.04820 http:\/\/arxiv.org\/abs\/1811.04820"},{"key":"e_1_3_2_2_3_1","volume-title":"Hullender","author":"Burges Christopher J. C.","year":"2005","unstructured":"Christopher J. C. Burges , Tal Shaked , Erin Renshaw , Ari Lazier , Matt Deeds , Nicole Hamilton , and Gregory N . Hullender . 2005 . Learning to rank using gradient descent. In ICML. 89--96. Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Gregory N. Hullender. 2005. Learning to rank using gradient descent. In ICML. 89--96."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271743"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Francesco De Comit\u00e9 Fran\u00e7ois Denis R\u00e9mi Gilleron and Fabien Letouzey. 1999. Positive and Unlabeled Examples Help Learning. In ALT. 219--230. Francesco De Comit\u00e9 Fran\u00e7ois Denis R\u00e9mi Gilleron and Fabien Letouzey. 1999. Positive and Unlabeled Examples Help Learning. In ALT. 219--230.","DOI":"10.1007\/3-540-46769-6_18"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_7_1","unstructured":"Boxin Du Lihui Liu and Hanghang Tong. 2021. Sylvester Tensor Equation for Multi-Way Association. In KDD. ACM. Boxin Du Lihui Liu and Hanghang Tong. 2021. Sylvester Tensor Equation for Multi-Way Association. In KDD. ACM."},{"key":"e_1_3_2_2_8_1","unstructured":"Marthinus Christoffel du Plessis Gang Niu and Masashi Sugiyama. 2014. Analysis of Learning from Positive and Unlabeled Data. In NeurIPS. 703--711. Marthinus Christoffel du Plessis Gang Niu and Masashi Sugiyama. 2014. Analysis of Learning from Positive and Unlabeled Data. In NeurIPS. 703--711."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Charles Elkan and Keith Noto. 2008. Learning classifiers from only positive and unlabeled data. In KDD. 213--220. Charles Elkan and Keith Noto. 2008. Learning classifiers from only positive and unlabeled data. In KDD. 213--220.","DOI":"10.1145\/1401890.1401920"},{"key":"e_1_3_2_2_10_1","unstructured":"Dongqi Fu Zhe Xu Bo Li Hanghang Tong and Jingrui He. 2020. A View-Adversarial Framework for Multi-View Network Embedding. In CIKM. Dongqi Fu Zhe Xu Bo Li Hanghang Tong and Jingrui He. 2020. A View-Adversarial Framework for Multi-View Network Embedding. In CIKM."},{"key":"e_1_3_2_2_11_1","unstructured":"Thomas George and Srujana Merugu. 2005. A Scalable Collaborative Filtering Framework Based on Co-Clustering. In ICDM. 625--628. Thomas George and Srujana Merugu. 2005. A Scalable Collaborative Filtering Framework Based on Co-Clustering. In ICDM. 625--628."},{"key":"e_1_3_2_2_12_1","unstructured":"Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS. 2672--2680. Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS. 2672--2680."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","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. 1725--1731. Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI. 1725--1731.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_14_1","unstructured":"Tianyu Guo Chang Xu Jiajun Huang Yunhe Wang Boxin Shi Chao Xu and Dacheng Tao. 2020. On Positive-Unlabeled Classification in GAN. In CVPR. 8382--8390. Tianyu Guo Chang Xu Jiajun Huang Yunhe Wang Boxin Shi Chao Xu and Dacheng Tao. 2020. On Positive-Unlabeled Classification in GAN. In CVPR. 8382--8390."},{"key":"e_1_3_2_2_15_1","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW. 173--182. Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW. 173--182."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Ming Hou Brahim Chaib-draa Chao Li and Qibin Zhao. 2018. Generative Adversarial Positive-Unlabelled Learning. In IJCAI. 2255--2261. Ming Hou Brahim Chaib-draa Chao Li and Qibin Zhao. 2018. Generative Adversarial Positive-Unlabelled Learning. In IJCAI. 2255--2261.","DOI":"10.24963\/ijcai.2018\/312"},{"key":"e_1_3_2_2_17_1","unstructured":"Cho-Jui Hsieh Nagarajan Natarajan and Inderjit S Dhillon. 2015. PU Learning for Matrix Completion. In ICML. 2445--2453. Cho-Jui Hsieh Nagarajan Natarajan and Inderjit S Dhillon. 2015. PU Learning for Matrix Completion. In ICML. 2445--2453."},{"key":"e_1_3_2_2_18_1","unstructured":"Binbin Hu Yuan Fang and Chuan Shi. 2019. Adversarial Learning on Heterogeneous Information Networks. In KDD. 120--129. Binbin Hu Yuan Fang and Chuan Shi. 2019. Adversarial Learning on Heterogeneous Information Networks. In KDD. 120--129."},{"key":"e_1_3_2_2_19_1","volume-title":"Measuring the Business Value of Recommender Systems. ACM TMIS 10, 4","author":"Jannach Dietmar","year":"2019","unstructured":"Dietmar Jannach and Michael Jugovac . 2019. Measuring the Business Value of Recommender Systems. ACM TMIS 10, 4 ( 2019 ). Dietmar Jannach and Michael Jugovac. 2019. Measuring the Business Value of Recommender Systems. ACM TMIS 10, 4 (2019)."},{"key":"e_1_3_2_2_20_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In ICLR. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_2_21_1","volume-title":"Marthinus Christoffel du Plessis, and Masashi Sugiyama","author":"Kiryo Ryuichi","year":"2017","unstructured":"Ryuichi Kiryo , Gang Niu , Marthinus Christoffel du Plessis, and Masashi Sugiyama . 2017 . Positive-Unlabeled Learning with Non-Negative Risk Estimator. In NeurIPS. 1675--1685. Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, and Masashi Sugiyama. 2017. Positive-Unlabeled Learning with Non-Negative Risk Estimator. In NeurIPS. 1675--1685."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. 426--434. Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. 426--434.","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In KDD. 1748--1757. Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In KDD. 1748--1757.","DOI":"10.1145\/3394486.3403226"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Daniel Lemire and Anna Maclachlan. 2005. Slope One Predictors for Online Rating-Based Collaborative Filtering. In SDM. 471--475. Daniel Lemire and Anna Maclachlan. 2005. Slope One Predictors for Online Rating-Based Collaborative Filtering. In SDM. 471--475.","DOI":"10.1137\/1.9781611972757.43"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Fabien Letouzey Fran\u00e7ois Denis and R\u00e9mi Gilleron. 2000. Learning from Positive and Unlabeled Examples. In ALT. 71--85. Fabien Letouzey Fran\u00e7ois Denis and R\u00e9mi Gilleron. 2000. Learning from Positive and Unlabeled Examples. In ALT. 71--85.","DOI":"10.1007\/3-540-40992-0_6"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Xu Liu Jingrui He Sam Duddy and Liz O'Sullivan. 2019. Convolution-Consistent Collective Matrix Completion. In CIKM. 2209--2212. Xu Liu Jingrui He Sam Duddy and Liz O'Sullivan. 2019. Convolution-Consistent Collective Matrix Completion. In CIKM. 2209--2212.","DOI":"10.1145\/3357384.3358111"},{"key":"e_1_3_2_2_28_1","first-page":"1273","article-title":"An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems","volume":"10","author":"Luo Xin","year":"2014","unstructured":"Xin Luo , Mengchu Zhou , Yunni Xia , and Qingsheng Zhu . 2014 . An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems . IEEE TII 10 , 2 (2014), 1273 -- 1284 . Xin Luo, Mengchu Zhou, Yunni Xia, and Qingsheng Zhu. 2014. An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems. IEEE TII 10, 2 (2014), 1273--1284.","journal-title":"IEEE TII"},{"key":"e_1_3_2_2_29_1","first-page":"139","article-title":"One-Class SVMs for Document Classification","volume":"2","author":"Manevitz Larry M.","year":"2001","unstructured":"Larry M. Manevitz and Malik Yousef . 2001 . One-Class SVMs for Document Classification . Journal of Machine Learning Research 2 (2001), 139 -- 154 . Larry M. Manevitz and Malik Yousef. 2001. One-Class SVMs for Document Classification. Journal of Machine Learning Research 2 (2001), 139--154.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_30_1","volume-title":"Tomoya Sakai, Yao Ma, and Masashi Sugiyama.","author":"Niu Gang","year":"2016","unstructured":"Gang Niu , Marthinus Christoffel du Plessis , Tomoya Sakai, Yao Ma, and Masashi Sugiyama. 2016 . Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. In NeurIPS. 1199--1207. Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, and Masashi Sugiyama. 2016. Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. In NeurIPS. 1199--1207."},{"key":"e_1_3_2_2_31_1","unstructured":"Rong Pan Yunhong Zhou Bin Cao Nathan Nan Liu Rajan M. Lukose Martin Scholz and Qiang Yang. [n.d.]. One-Class Collaborative Filtering. In ICDM. Rong Pan Yunhong Zhou Bin Cao Nathan Nan Liu Rajan M. Lukose Martin Scholz and Qiang Yang. [n.d.]. One-Class Collaborative Filtering. In ICDM."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Dae Hoon Park and Yi Chang. 2019. Adversarial Sampling and Training for Semi-Supervised Information Retrieval. In WWW. 1443--1453. Dae Hoon Park and Yi Chang. 2019. Adversarial Sampling and Training for Semi-Supervised Information Retrieval. In WWW. 1443--1453.","DOI":"10.1145\/3308558.3313416"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle. 2010. Factorization Machines. In ICDM. 995--1000. Steffen Rendle. 2010. Factorization Machines. In ICDM. 995--1000.","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_2_34_1","volume-title":"BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI."},{"key":"e_1_3_2_2_35_1","unstructured":"Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NeurIPS. 1257--1264. Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NeurIPS. 1257--1264."},{"key":"e_1_3_2_2_36_1","volume-title":"Abdelzaher","author":"Shao Huajie","year":"2020","unstructured":"Huajie Shao , Dachun Sun , Jiahao Wu , Zecheng Zhang , Shuochao Yao , Shengzhong Liu , Tianshi Wang , Chao Zhang , and Tarek F . Abdelzaher . 2020 . paper2repo: GitHub Repository Recommendation for Academic Papers . In WWW. 629--639. Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, and Tarek F. Abdelzaher. 2020. paper2repo: GitHub Repository Recommendation for Academic Papers. In WWW. 629--639."},{"key":"e_1_3_2_2_37_1","volume-title":"Anh Tuan Luu, and Siu Cheung Hui","author":"Tay Yi","year":"2018","unstructured":"Yi Tay , Anh Tuan Luu, and Siu Cheung Hui . 2018 . Multi-Pointer Co-Attention Networks for Recommendation. In KDD. 2309--2318. Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. In KDD. 2309--2318."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Hongwei Wang Jia Wang Jialin Wang Miao Zhao Weinan Zhang Fuzheng Zhang Xing Xie and Minyi Guo. 2018. GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In AAAI. 2508--2515. Hongwei Wang Jia Wang Jialin Wang Miao Zhao Weinan Zhang Fuzheng Zhang Xing Xie and Minyi Guo. 2018. GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In AAAI. 2508--2515.","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Haonan Wang Chang Zhou Hongxia Yang Carl Yang and Jingrui He. 2021. Controllable Gradient Item Retrieval. In WWW. Haonan Wang Chang Zhou Hongxia Yang Carl Yang and Jingrui He. 2021. Controllable Gradient Item Retrieval. In WWW.","DOI":"10.1145\/3442381.3449963"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Da Xu Chuanwei Ruan Jason Cho Evren Korpeoglu Sushant Kumar and Kannan Achan. 2020. Knowledge-Aware Complementary Product Representation Learning. In WSDM. 681--689. Da Xu Chuanwei Ruan Jason Cho Evren Korpeoglu Sushant Kumar and Kannan Achan. 2020. Knowledge-Aware Complementary Product Representation Learning. In WSDM. 681--689.","DOI":"10.1145\/3336191.3371854"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Guibing Guo Joemon M. Jose Long Chen Haitao Yu and Weinan Zhang. 2016. LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates. In CIKM. 227--236. Fajie Yuan Guibing Guo Joemon M. Jose Long Chen Haitao Yu and Weinan Zhang. 2016. LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates. In CIKM. 227--236.","DOI":"10.1145\/2983323.2983758"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Si Zhang Hanghang Tong Yinglong Xia Liang Xiong and Jiejun Xu. 2020. NetTrans: Neural Cross-Network Transformation. In KDD. 986--996. Si Zhang Hanghang Tong Yinglong Xia Liang Xiong and Jiejun Xu. 2020. NetTrans: Neural Cross-Network Transformation. In KDD. 986--996.","DOI":"10.1145\/3394486.3403141"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3285029"},{"key":"e_1_3_2_2_45_1","volume-title":"Deep Co-Attention Network for Multi-View Subspace Learning. CoRR abs\/2102.07751","author":"Zheng Lecheng","year":"2021","unstructured":"Lecheng Zheng , Yu Cheng , Hongxia Yang , Nan Cao , and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. CoRR abs\/2102.07751 ( 2021 ). Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. CoRR abs\/2102.07751 (2021)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2019.00003"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Yao Zhou Fenglong Ma Jing Gao and Jingrui He. 2019. Optimizing the Wisdom of the Crowd: Inference Learning and Teaching. In KDD. 3231--3232. Yao Zhou Fenglong Ma Jing Gao and Jingrui He. 2019. Optimizing the Wisdom of the Crowd: Inference Learning and Teaching. In KDD. 3231--3232.","DOI":"10.1145\/3292500.3332277"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Yao Zhou Lei Ying and Jingrui He. 2017. MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. In SDM. 579--587. Yao Zhou Lei Ying and Jingrui He. 2017. MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. In SDM. 579--587.","DOI":"10.1137\/1.9781611974973.65"},{"key":"e_1_3_2_2_49_1","volume-title":"Multi-task Crowdsourcing via an Optimization Framework. ACM TKDD 13, 3","author":"Zhou Yao","year":"2019","unstructured":"Yao Zhou , Lei Ying , and Jingrui He. 2019. Multi-task Crowdsourcing via an Optimization Framework. ACM TKDD 13, 3 ( 2019 ), 27:1--27:26. Yao Zhou, Lei Ying, and Jingrui He. 2019. Multi-task Crowdsourcing via an Optimization Framework. ACM TKDD 13, 3 (2019), 27:1--27:26."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467234","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3447548.3467234","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467234","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467234"}},"subtitle":["Positive-Unlabeled Recommendation with Generative Adversarial Network"],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":49,"alternative-id":["10.1145\/3447548.3467234","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467234","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}