{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:54:49Z","timestamp":1783439689603,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Charles Koch Foundation"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2146171 ; IIS-1917713"],"award-info":[{"award-number":["CNS-2146171 ; IIS-1917713"]}],"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":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557108","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"3033-3042","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning"],"prefix":"10.1145","author":[{"given":"Yun-Wei","family":"Chu","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyedali","family":"Hosseinalipour","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Tenorio","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Cruz","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerrie","family":"Douglas","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Lan","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Brinton","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments","author":"Adedoyin Olasile Babatunde","year":"2020","unstructured":"Olasile Babatunde Adedoyin and Emrah Soykan . 2020. Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments ( 2020 ), 1--13. Olasile Babatunde Adedoyin and Emrah Soykan. 2020. Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments (2020), 1--13."},{"key":"e_1_3_2_1_2_1","volume-title":"Sehoya Cotner, and Cissy J. Ballen.","author":"Aguillon Stepfanie M.","year":"2020","unstructured":"Stepfanie M. Aguillon , Gregor-Fausto Siegmund , Renee H. Petipas , Abby Grace Drake , Sehoya Cotner, and Cissy J. Ballen. 2020 . Gender Differences in Student Participation in an Active-Learning Classroom. CBE Life Sci. Edu ., Vol. 19 (2020). Stepfanie M. Aguillon, Gregor-Fausto Siegmund, Renee H. Petipas, Abby Grace Drake, Sehoya Cotner, and Cissy J. Ballen. 2020. Gender Differences in Student Participation in an Active-Learning Classroom. CBE Life Sci. Edu., Vol. 19 (2020)."},{"key":"e_1_3_2_1_3_1","volume-title":"PROPUBLICA","volume":"28","author":"Angwin Julia","year":"2017","unstructured":"Julia Angwin and Hannes Grassegger . 2017 . Facebook's secret censorship rules protect white men from hate speech but not black children . PROPUBLICA , Vol. 28 (2017). Julia Angwin and Hannes Grassegger. 2017. Facebook's secret censorship rules protect white men from hate speech but not black children. PROPUBLICA, Vol. 28 (2017)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376518"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-61425-0_4"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2546228"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2859325"},{"key":"e_1_3_2_1_9_1","volume-title":"IEEE Int. Conf. Computer Comms.","author":"Brinton Christopher G.","year":"2015","unstructured":"Christopher G. Brinton and M. Chiang . 2015. MOOC performance prediction via clickstream data and social learning networks . IEEE Int. Conf. Computer Comms. ( 2015 ), 2299--2307. Christopher G. Brinton and M. Chiang. 2015. MOOC performance prediction via clickstream data and social learning networks. IEEE Int. Conf. Computer Comms. (2015), 2299--2307."},{"key":"e_1_3_2_1_10_1","volume-title":"Conf. Fairness, Accountability and Transparency. 77--91","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru . 2018 . Gender shades: Intersectional accuracy disparities in commercial gender classification . In Conf. Fairness, Accountability and Transparency. 77--91 . Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conf. Fairness, Accountability and Transparency. 77--91."},{"key":"e_1_3_2_1_11_1","volume-title":"Clickstream Knowledge Tracing: Modeling How Students Answer Interactive Online Questions. In Int. Learn. Analytics and Knowl. Conf. 99--109","author":"Chan Wai-Lun","year":"2021","unstructured":"Wai-Lun Chan and Dit-Yan Yeung . 2021 . Clickstream Knowledge Tracing: Modeling How Students Answer Interactive Online Questions. In Int. Learn. Analytics and Knowl. Conf. 99--109 . Wai-Lun Chan and Dit-Yan Yeung. 2021. Clickstream Knowledge Tracing: Modeling How Students Answer Interactive Online Questions. In Int. Learn. Analytics and Knowl. Conf. 99--109."},{"key":"e_1_3_2_1_12_1","volume-title":"Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams","author":"Chen Weiyu","year":"2018","unstructured":"Weiyu Chen , Andrew S Lan , Da Cao , Christopher Brinton , and Mung Chiang . 2018. Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams . International Educational Data Mining Society ( 2018 ). Weiyu Chen, Andrew S Lan, Da Cao, Christopher Brinton, and Mung Chiang. 2018. Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. International Educational Data Mining Society (2018)."},{"key":"e_1_3_2_1_13_1","unstructured":"Shivangi Chopra Hannah Gautreau Abeer Khan Melicaalsadat Mirsafian and Lukasz Golab. 2018. Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach. In EDM.  Shivangi Chopra Hannah Gautreau Abeer Khan Melicaalsadat Mirsafian and Lukasz Golab. 2018. Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach. In EDM."},{"key":"e_1_3_2_1_14_1","volume-title":"Click-Based Student Performance Prediction: A Clustering Guxided Meta-Learning Approach. IEEE Int. Conf. Big Data","author":"Chu Yun-Wei","year":"2021","unstructured":"Yun-Wei Chu , Elizabeth Tenorio , Laura Cruz , Kerrie Anna Douglas , Andrew S. Lan , and Christopher G. Brinton . 2021 . Click-Based Student Performance Prediction: A Clustering Guxided Meta-Learning Approach. IEEE Int. Conf. Big Data ( 2021 ), 1389--1398. Yun-Wei Chu, Elizabeth Tenorio, Laura Cruz, Kerrie Anna Douglas, Andrew S. Lan, and Christopher G. Brinton. 2021. Click-Based Student Performance Prediction: A Clustering Guxided Meta-Learning Approach. IEEE Int. Conf. Big Data (2021), 1389--1398."},{"key":"e_1_3_2_1_15_1","volume-title":"Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction","author":"Corbett Albert T","year":"1994","unstructured":"Albert T Corbett and John R Anderson . 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction , Vol. 4 , 4 ( 1994 ), 253--278. Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, Vol. 4, 4 (1994), 253--278."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2013.09.027"},{"key":"e_1_3_2_1_17_1","unstructured":"Alireza Fallah Aryan Mokhtari and A. Ozdaglar. 2020. Personalized Federated Learning: A Meta-Learning Approach. ArXiv Vol. abs\/2002.07948 (2020).  Alireza Fallah Aryan Mokhtari and A. Ozdaglar. 2020. Personalized Federated Learning: A Meta-Learning Approach. ArXiv Vol. abs\/2002.07948 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0209749"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3303772.3303791"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"e_1_3_2_1_21_1","volume-title":"Learning Private Neural Language Modeling with Attentive Aggregation. Int. Joint Conf. Neural Netw.","author":"Ji Shaoxiong","year":"2019","unstructured":"Shaoxiong Ji , Shirui Pan , Guodong Long , Xue Li , Jing Jiang , and Zi Huang . 2019 . Learning Private Neural Language Modeling with Attentive Aggregation. Int. Joint Conf. Neural Netw. (2019), 1--8. Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang. 2019. Learning Private Neural Language Modeling with Attentive Aggregation. Int. Joint Conf. Neural Netw. (2019), 1--8."},{"key":"e_1_3_2_1_22_1","unstructured":"Yihan Jiang Jakub Konecn\u00fd Keith Rush and S. Kannan. 2019. Improving Federated Learning Personalization via Model Agnostic Meta Learning. ArXiv Vol. abs\/1909.12488 (2019).  Yihan Jiang Jakub Konecn\u00fd Keith Rush and S. Kannan. 2019. Improving Federated Learning Personalization via Model Agnostic Meta Learning. ArXiv Vol. abs\/1909.12488 (2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448139.3448154"},{"key":"e_1_3_2_1_24_1","volume-title":"Int. Conf. Machine Learn. PMLR, 2564--2572","author":"Kearns Michael","year":"2018","unstructured":"Michael Kearns , Seth Neel , Aaron Roth , and Zhiwei Steven Wu . 2018 . Preventing fairness gerrymandering: Auditing and learning for subgroup fairness . In Int. Conf. Machine Learn. PMLR, 2564--2572 . Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In Int. Conf. Machine Learn. PMLR, 2564--2572."},{"key":"e_1_3_2_1_25_1","volume-title":"Algorithmic fairness in education. arXiv preprint arXiv:2007.05443","author":"Kizilcec Ren\u00e9 F","year":"2020","unstructured":"Ren\u00e9 F Kizilcec and Hansol Lee . 2020. Algorithmic fairness in education. arXiv preprint arXiv:2007.05443 ( 2020 ). Ren\u00e9 F Kizilcec and Hansol Lee. 2020. Algorithmic fairness in education. arXiv preprint arXiv:2007.05443 (2020)."},{"key":"e_1_3_2_1_26_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. ArXiv","author":"Konecn\u00fd Jakub","year":"2016","unstructured":"Jakub Konecn\u00fd , H. B. McMahan , F. Yu , Peter Richt\u00e1rik , A. T. Suresh , and D. Bacon . 2016 . Federated Learning: Strategies for Improving Communication Efficiency. ArXiv , Vol. abs\/ 1610 .05492 (2016). Jakub Konecn\u00fd, H. B. McMahan, F. Yu, Peter Richt\u00e1rik, A. T. Suresh, and D. Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. ArXiv, Vol. abs\/1610.05492 (2016)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00121"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1177\/0956797613504302"},{"key":"e_1_3_2_1_29_1","unstructured":"Jessica McBroom Irena Koprinska and Kalina Yacef. 2020. How Does Student Behaviour Change Approaching Dropout? A Study of Gender and School Year Differences. In EDM.  Jessica McBroom Irena Koprinska and Kalina Yacef. 2020. How Does Student Behaviour Change Approaching Dropout? A Study of Gender and School Year Differences. In EDM."},{"key":"e_1_3_2_1_30_1","volume-title":"Int. Conf. Artif. Intell. and Stats.","author":"McMahan H. B.","year":"2017","unstructured":"H. B. McMahan , Eider Moore , D. Ramage , S. Hampson , and B. A. Y. Arcas . 2017 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In Int. Conf. Artif. Intell. and Stats. H. B. McMahan, Eider Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Int. Conf. Artif. Intell. and Stats."},{"key":"e_1_3_2_1_31_1","volume-title":"Efficient Estimation of Word Representations in Vector Space. In Int. Conf. Learn. Represent.","author":"Mikolov Tomas","unstructured":"Tomas Mikolov , Kai Chen , G. Corrado , and J. Dean . 2013 . Efficient Estimation of Word Representations in Vector Space. In Int. Conf. Learn. Represent. Tomas Mikolov, Kai Chen, G. Corrado, and J. Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In Int. Conf. Learn. Represent."},{"key":"e_1_3_2_1_32_1","volume-title":"Ballen","author":"Neill Connor","year":"2019","unstructured":"Connor Neill , Sehoya Cotner , M. Driessen , and Cissy J . Ballen . 2019 . Structured learning environments are required to promote equitable participation. Chemistry Education Research and Practice ( 2019). Connor Neill, Sehoya Cotner, M. Driessen, and Cissy J. Ballen. 2019. Structured learning environments are required to promote equitable participation. Chemistry Education Research and Practice (2019)."},{"key":"e_1_3_2_1_33_1","first-page":"1","article-title":"Who's Learning? Using Demographics in EDM Research","volume":"12","author":"Paquette Luc","year":"2020","unstructured":"Luc Paquette , Jaclyn Ocumpaugh , Ziyue Li , Alexandra Andres , and Ryan Baker . 2020 . Who's Learning? Using Demographics in EDM Research . Journal of Educational Data Mining , Vol. 12 , 3 (2020), 1 -- 30 . Luc Paquette, Jaclyn Ocumpaugh, Ziyue Li, Alexandra Andres, and Ryan Baker. 2020. Who's Learning? Using Demographics in EDM Research. Journal of Educational Data Mining, Vol. 12, 3 (2020), 1--30.","journal-title":"Journal of Educational Data Mining"},{"key":"e_1_3_2_1_34_1","unstructured":"C. Piech J. Bassen J. Huang S. Ganguli M. Sahami L. Guibas and J. Sohl-Dickstein. 2015. Deep knowledge tracing. In Adv. Neural Inf. Process. Syst. 505--513.  C. Piech J. Bassen J. Huang S. Ganguli M. Sahami L. Guibas and J. Sohl-Dickstein. 2015. Deep knowledge tracing. In Adv. Neural Inf. Process. Syst. 505--513."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939850"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.2307\/2283550"},{"key":"e_1_3_2_1_37_1","volume-title":"Are Pre-trained Convolutions Better than Pre-trained Transformers? ArXiv","author":"Tay Yi","year":"2021","unstructured":"Yi Tay , Mostafa Dehghani , Jai Gupta , Dara Bahri , V. Aribandi , Zhen Qin , and Donald Metzler . 2021. Are Pre-trained Convolutions Better than Pre-trained Transformers? ArXiv , Vol. abs\/ 2105 .03322 ( 2021 ). Yi Tay, Mostafa Dehghani, Jai Gupta, Dara Bahri, V. Aribandi, Zhen Qin, and Donald Metzler. 2021. Are Pre-trained Convolutions Better than Pre-trained Transformers? ArXiv, Vol. abs\/2105.03322 (2021)."},{"key":"e_1_3_2_1_38_1","volume-title":"Handbook of modern item response theory","author":"van der Linden Wim J","unstructured":"Wim J van der Linden and Ronald K Hambleton . 2013. Handbook of modern item response theory . Springer Science and Business Media . Wim J van der Linden and Ronald K Hambleton. 2013. Handbook of modern item response theory. Springer Science and Business Media."},{"key":"e_1_3_2_1_39_1","first-page":"78","article-title":"Student modeling","volume":"55","author":"VanLehn Kurt","year":"1988","unstructured":"Kurt VanLehn . 1988 . Student modeling . Found. Intell. Tut. Syst. , Vol. 55 (1988), 78 . Kurt VanLehn. 1988. Student modeling. Found. Intell. Tut. Syst., Vol. 55 (1988), 78.","journal-title":"Found. Intell. Tut. Syst."},{"key":"e_1_3_2_1_40_1","volume-title":"Attention is All you Need. ArXiv","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam M. Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention is All you Need. ArXiv , Vol. abs\/ 1706 .03762 ( 2017 ). Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. ArXiv, Vol. abs\/1706.03762 (2017)."},{"key":"e_1_3_2_1_41_1","volume-title":"Pay Less Attention with Lightweight and Dynamic Convolutions. ArXiv","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Angela Fan , Alexei Baevski , Yann Dauphin , and Michael Auli . 2019. Pay Less Attention with Lightweight and Dynamic Convolutions. ArXiv , Vol. abs\/ 1901 .10430 ( 2019 ). Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, and Michael Auli. 2019. Pay Less Attention with Lightweight and Dynamic Convolutions. ArXiv, Vol. abs\/1901.10430 (2019)."},{"key":"e_1_3_2_1_42_1","volume-title":"Federated Deep Knowledge Tracing. ACM Int. Conf. Web Search and Data Mining","author":"Wu Jinze","year":"2021","unstructured":"Jinze Wu , Zhenya Huang , Qi Liu , Defu Lian , H. Wang , Enhong Chen , Haiping Ma , and Shijin Wang . 2021 . Federated Deep Knowledge Tracing. ACM Int. Conf. Web Search and Data Mining (2021). Jinze Wu, Zhenya Huang, Qi Liu, Defu Lian, H. Wang, Enhong Chen, Haiping Ma, and Shijin Wang. 2021. Federated Deep Knowledge Tracing. ACM Int. Conf. Web Search and Data Mining (2021)."},{"key":"e_1_3_2_1_43_1","volume-title":"Data-driven Educ. WRKSH Conf. Neural Inf. Process. Syst.","volume":"11","author":"Yang Diyi","year":"2013","unstructured":"Diyi Yang , Tanmay Sinha , David Adamson , and Carolyn Penstein Ros\u00e9 . 2013 . Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses . In Data-driven Educ. WRKSH Conf. Neural Inf. Process. Syst. , Vol. 11 . 14. Diyi Yang, Tanmay Sinha, David Adamson, and Carolyn Penstein Ros\u00e9. 2013. Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Data-driven Educ. WRKSH Conf. Neural Inf. Process. Syst., Vol. 11. 14."},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019","author":"Yang Tsung-Yen","year":"2019","unstructured":"Tsung-Yen Yang , Ryan S Baker , Christoph Studer , Neil Heffernan , and Andrew S Lan . 2019 . Active learning for student affect detection . In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019 , Montr\u00e9al, Canada, July 2--5 , 2019. International Educational Data Mining Society (IEDMS) 2019. Universit\u00e9 du Qu\u00e9bec; Polytechnique Montr\u00e9al, 208--217. Tsung-Yen Yang, Ryan S Baker, Christoph Studer, Neil Heffernan, and Andrew S Lan. 2019. Active learning for student affect detection. In Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019, Montr\u00e9al, Canada, July 2--5, 2019. International Educational Data Mining Society (IEDMS) 2019. Universit\u00e9 du Qu\u00e9bec; Polytechnique Montr\u00e9al, 208--217."},{"key":"e_1_3_2_1_45_1","volume-title":"IEEE Int. Conf. Big Data","author":"Yang Tsung-Yen","year":"2018","unstructured":"Tsung-Yen Yang , Christopher G. Brinton , Prateek Mittal , M. Chiang , and Andrew S. Lan . 2018. Learning Informative and Private Representations via Generative Adversarial Networks . IEEE Int. Conf. Big Data ( 2018 ), 1534--1543. Tsung-Yen Yang, Christopher G. Brinton, Prateek Mittal, M. Chiang, and Andrew S. Lan. 2018. Learning Informative and Private Representations via Generative Adversarial Networks. IEEE Int. Conf. Big Data (2018), 1534--1543."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450104"},{"key":"e_1_3_2_1_47_1","volume-title":"Beyond parity: Fairness objectives for collaborative filtering. arXiv preprint arXiv:1705.08804","author":"Yao Sirui","year":"2017","unstructured":"Sirui Yao and Bert Huang . 2017. Beyond parity: Fairness objectives for collaborative filtering. arXiv preprint arXiv:1705.08804 ( 2017 ). Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. arXiv preprint arXiv:1705.08804 (2017)."},{"key":"e_1_3_2_1_48_1","volume-title":"Dynamic Key-Value Memory Networks for Knowledge Tracing. The World Wide Web Conf.","author":"Zhang Jiani","year":"2017","unstructured":"Jiani Zhang , Xingjian Shi , Irwin King , and D. Yeung . 2017 . Dynamic Key-Value Memory Networks for Knowledge Tracing. The World Wide Web Conf. ( 2017 ). Jiani Zhang, Xingjian Shi, Irwin King, and D. Yeung. 2017. Dynamic Key-Value Memory Networks for Knowledge Tracing. The World Wide Web Conf. (2017)."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557108","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557108","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:56Z","timestamp":1750188656000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":48,"alternative-id":["10.1145\/3511808.3557108","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557108","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}