{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:43Z","timestamp":1750220443402,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":70,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Google Student Research Advisorship Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441784","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:34:28Z","timestamp":1615005268000},"page":"1002-1010","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Neural Recommender Models through Domain-Specific Concordance"],"prefix":"10.1145","author":[{"given":"Ananth","family":"Balashankar","sequence":"first","affiliation":[{"name":"New York University & Google AI, New York, NY, USA"}]},{"given":"Alex","family":"Beutel","sequence":"additional","affiliation":[{"name":"Google AI, New York, NY, USA"}]},{"given":"Lakshminarayanan","family":"Subramanian","sequence":"additional","affiliation":[{"name":"New York University, New York, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"volume-title":"ISWC'07\/ASWC'07","author":"Auer S.","key":"e_1_3_2_1_1_1","unstructured":"S. Auer , C. Bizer , G. Kobilarov , J. Lehmann , R. Cyganiak , and Z. Ives . 2007. DBpedia: A Nucleus for a Web of Open Data . In ISWC'07\/ASWC'07 ( Busan, Korea). 722--735. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives. 2007. DBpedia: A Nucleus for a Web of Open Data. In ISWC'07\/ASWC'07 (Busan, Korea). 722--735."},{"key":"e_1_3_2_1_2_1","volume-title":"The continuous cold start problem in e-commerce recommender systems. arXiv:1508.01177","author":"Bernardi L.","year":"2015","unstructured":"L. Bernardi , J. Kamps , J. Kiseleva , and M. JI M\u00fcller . 2015. The continuous cold start problem in e-commerce recommender systems. arXiv:1508.01177 ( 2015 ). L. Bernardi, J. Kamps, J. Kiseleva, and M. JI M\u00fcller. 2015. The continuous cold start problem in e-commerce recommender systems. arXiv:1508.01177 (2015)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"A. Beutel J. Chen T. Doshi H. Qian L. Wei Y. Wu L. Heldt Z. Zhao L. Hong E. H. Chi and C. Goodrow. 2019. Fairness in Recommendation Ranking through Pairwise Comparisons. KDD (2019) 2212--2220.  A. Beutel J. Chen T. Doshi H. Qian L. Wei Y. Wu L. Heldt Z. Zhao L. Hong E. H. Chi and C. Goodrow. 2019. Fairness in Recommendation Ranking through Pairwise Comparisons. KDD (2019) 2212--2220.","DOI":"10.1145\/3292500.3330745"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"A. J. Biega K. P. Gummadi and G. Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In SIGIR . ACM 405--414.  A. J. Biega K. P. Gummadi and G. Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In SIGIR . ACM 405--414.","DOI":"10.1145\/3209978.3210063"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"C. J. Cai E. Reif N. Hegde J. Hipp B. Kim D. Smilkov M Wattenberg F. Viegas G. S. Corrado M. C. Stumpe and M. Terry. 2019. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. In CHI .  C. J. Cai E. Reif N. Hegde J. Hipp B. Kim D. Smilkov M Wattenberg F. Viegas G. S. Corrado M. C. Stumpe and M. Terry. 2019. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. In CHI .","DOI":"10.1145\/3290605.3300234"},{"key":"e_1_3_2_1_6_1","unstructured":"Y. Carmon A. Raghunathan L. Schmidt J. C. Duchi and P. S Liang. 2019. Unlabeled data improves adversarial robustness. In NeurIPS. 11192--11203.  Y. Carmon A. Raghunathan L. Schmidt J. C. Duchi and P. S Liang. 2019. Unlabeled data improves adversarial robustness. In NeurIPS. 11192--11203."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"K. Christakopoulou and A. Banerjee. 2019. Adversarial Attacks on an Oblivious Recommender. In RecSys (Copenhagen Denmark). 322--330.  K. Christakopoulou and A. Banerjee. 2019. Adversarial Attacks on an Oblivious Recommender. In RecSys (Copenhagen Denmark). 322--330.","DOI":"10.1145\/3298689.3347031"},{"key":"e_1_3_2_1_8_1","unstructured":"J. M. Cohen E. Rosenfeld and J. Z. Kolter. 2019. Certified adversarial robustness via randomized smoothing. arXiv preprint arXiv:1902.02918 (2019).  J. M. Cohen E. Rosenfeld and J. Z. Kolter. 2019. Certified adversarial robustness via randomized smoothing. arXiv preprint arXiv:1902.02918 (2019)."},{"key":"e_1_3_2_1_9_1","volume-title":"Recommender Systems Fairness Evaluation via Generalized Cross Entropy. arxiv","author":"Deldjoo Y.","year":"1908","unstructured":"Y. Deldjoo , V. W. Anelli , H. Zamani , A. Bellogin , and T. Di Noia . 2019. Recommender Systems Fairness Evaluation via Generalized Cross Entropy. arxiv : 1908 .06708 Y. Deldjoo, V. W. Anelli, H. Zamani, A. Bellogin, and T. Di Noia. 2019. Recommender Systems Fairness Evaluation via Generalized Cross Entropy. arxiv: 1908.06708"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Y. Deldjoo T. Di Noia E. Di Sciascio and F. A. Merra. 2020. How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. In SIGIR (Virtual Event China). 951--960.  Y. Deldjoo T. Di Noia E. Di Sciascio and F. A. Merra. 2020. How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. In SIGIR (Virtual Event China). 951--960.","DOI":"10.1145\/3397271.3401046"},{"key":"e_1_3_2_1_11_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT.","author":"Devlin J.","year":"2019","unstructured":"J. Devlin , M.W. Chang , K. Lee , and K. Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. J. Devlin, M.W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Y. Ding J. Liu and D. Wang. 2018. Deep Feature Fusion over Multi-Field Categorical Data for Rating Prediction. In AICCC (Tokyo Japan). 16--22.  Y. Ding J. Liu and D. Wang. 2018. Deep Feature Fusion over Multi-Field Categorical Data for Rating Prediction. In AICCC (Tokyo Japan). 16--22.","DOI":"10.1145\/3299819.3299827"},{"key":"e_1_3_2_1_13_1","volume-title":"Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim . 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 ( 2017 ). Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1906831117"},{"key":"e_1_3_2_1_15_1","volume-title":"PRINCE: Provider-Side Interpretability with Counterfactual Explanations in Recommender Systems. In WSDM (Houston, TX, USA). 196--204.","author":"Ghazimatin A.","year":"2020","unstructured":"A. Ghazimatin , O. Balalau , R. Saha Roy , and G. Weikum . 2020 . PRINCE: Provider-Side Interpretability with Counterfactual Explanations in Recommender Systems. In WSDM (Houston, TX, USA). 196--204. A. Ghazimatin, O. Balalau, R. Saha Roy, and G. Weikum. 2020. PRINCE: Provider-Side Interpretability with Counterfactual Explanations in Recommender Systems. In WSDM (Houston, TX, USA). 196--204."},{"key":"e_1_3_2_1_16_1","unstructured":"S. Gowal K. Dvijotham R. Stanforth R. Bunel C. Qin J. Uesato R. Arandjelovic T. A. Mann and P. Kohli. 2018. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. (2018). arxiv: 1810.12715  S. Gowal K. Dvijotham R. Stanforth R. Bunel C. Qin J. Uesato R. Arandjelovic T. A. Mann and P. Kohli. 2018. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. (2018). arxiv: 1810.12715"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"A. Gunawardana and C. Meek. 2009. A unified approach to building hybrid recommender systems. In RecSys. 117--124.  A. Gunawardana and C. Meek. 2009. A unified approach to building hybrid recommender systems. In RecSys. 117--124.","DOI":"10.1145\/1639714.1639735"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"G. Hadash O. S. Shalom and R. Osadchy. 2018. Rank and rate: multi-task learning for recommender systems. In RecSys. 451--454.  G. Hadash O. S. Shalom and R. Osadchy. 2018. Rank and rate: multi-task learning for recommender systems. In RecSys. 451--454.","DOI":"10.1145\/3240323.3240406"},{"key":"e_1_3_2_1_19_1","first-page":"19","volume-title":"Konstan","author":"Maxwell Harper F.","year":"2015","unstructured":"F. Maxwell Harper and Joseph A . Konstan . 2015 . The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. , Vol. 5 , 4, Article 19 (Dec. 2015), bibinfonumpages 19 pages. F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. , Vol. 5, 4, Article 19 (Dec. 2015), bibinfonumpages19 pages."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3317596"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"X. He Z. He X. Du and T.S. Chua. 2018. Adversarial Personalized Ranking for Recommendation. In SIGIR . 355--364.  X. He Z. He X. Du and T.S. Chua. 2018. Adversarial Personalized Ranking for Recommendation. In SIGIR . 355--364.","DOI":"10.1145\/3209978.3209981"},{"key":"e_1_3_2_1_22_1","unstructured":"G. Hinton O. Vinyals and J. Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).  G. Hinton O. Vinyals and J. Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Y.L. Hsieh M. Cheng D.C. Juan W. Wei W.L. Hsu and C.J. Hsieh. 2019. On the Robustness of Self-Attentive Models. In ACL. Florence Italy 1520--1529.  Y.L. Hsieh M. Cheng D.C. Juan W. Wei W.L. Hsu and C.J. Hsieh. 2019. On the Robustness of Self-Attentive Models. In ACL. Florence Italy 1520--1529.","DOI":"10.18653\/v1\/P19-1147"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"N. J. Hurley. 2011. Robustness of Recommender Systems. In RecSys. 9--10.  N. J. Hurley. 2011. Robustness of Recommender Systems. In RecSys. 9--10.","DOI":"10.1145\/2043932.2043937"},{"key":"e_1_3_2_1_25_1","first-page":"125","article-title":"Adversarial Examples Are Not Bugs, They Are Features","volume":"32","author":"Ilyas A","year":"2019","unstructured":"A Ilyas , S Santurkar , D Tsipras , L Engstrom , B Tran , and A Madry . 2019 . Adversarial Examples Are Not Bugs, They Are Features . In NeurIPS , Vol. 32. 125 -- 136 . A Ilyas, S Santurkar, D Tsipras, L Engstrom, B Tran, and A Madry. 2019. Adversarial Examples Are Not Bugs, They Are Features. In NeurIPS, Vol. 32. 125--136.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"R. Jia A. Raghunathan K. G\u00f6ksel and P. Liang. 2019. Certified Robustness to Adversarial Word Substitutions. EMNLP-IJCNLP (2019).  R. Jia A. Raghunathan K. G\u00f6ksel and P. Liang. 2019. Certified Robustness to Adversarial Word Substitutions. EMNLP-IJCNLP (2019).","DOI":"10.18653\/v1\/D19-1423"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"W.C. Kang D. Z. Cheng T. Chen X. Yi D. Lin L. Hong and E. H. Chi. 2020. Learning Multi-Granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. In WWW (Taipei Taiwan). 562--566.  W.C. Kang D. Z. Cheng T. Chen X. Yi D. Lin L. Hong and E. H. Chi. 2020. Learning Multi-Granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. In WWW (Taipei Taiwan). 562--566.","DOI":"10.1145\/3366424.3383416"},{"key":"e_1_3_2_1_28_1","volume-title":"Adversarial logit pairing. arXiv:1803.06373","author":"Kannan H","year":"2018","unstructured":"H Kannan , A Kurakin , and I Goodfellow . 2018. Adversarial logit pairing. arXiv:1803.06373 ( 2018 ). H Kannan, A Kurakin, and I Goodfellow. 2018. Adversarial logit pairing. arXiv:1803.06373 (2018)."},{"key":"e_1_3_2_1_29_1","unstructured":"D. Kaushik E. Hovy and Z. C. Lipton. 2019. Learning the difference that makes a difference with counterfactually-augmented data. arXiv:1909.12434 (2019).  D. Kaushik E. Hovy and Z. C. Lipton. 2019. Learning the difference that makes a difference with counterfactually-augmented data. arXiv:1909.12434 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"His Own: How Different Users Call for Different Interaction Methods in Recommender Systems. In RecSys (Chicago, Illinois, USA). 141--148.","author":"Knijnenburg B. P.","year":"2011","unstructured":"B. P. Knijnenburg , N. J.M. Reijmer , and M. C. Willemsen . 2011 . Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems. In RecSys (Chicago, Illinois, USA). 141--148. B. P. Knijnenburg, N. J.M. Reijmer, and M. C. Willemsen. 2011. Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems. In RecSys (Chicago, Illinois, USA). 141--148."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1080\/08839519308949993"},{"key":"e_1_3_2_1_32_1","unstructured":"S. Krichene M. Gartrell and C. Calauz\u00e8nes. 2019. Embedding models for recommendation under contextual constraints. arXiv Vol. abs\/1907.01637 (2019).  S. Krichene M. Gartrell and C. Calauz\u00e8nes. 2019. Embedding models for recommendation under contextual constraints. arXiv Vol. abs\/1907.01637 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-012-9896-1"},{"key":"e_1_3_2_1_34_1","unstructured":"Z. C. Lipton. 2016. The Mythos of Model Interpretability. (2016). arxiv: 1606.03490  Z. C. Lipton. 2016. The Mythos of Model Interpretability. (2016). arxiv: 1606.03490"},{"key":"e_1_3_2_1_35_1","unstructured":"P. Massa and P. Avesani. [n.d.]. Trust-aware recommender systems. In RecSys .  P. Massa and P. Avesani. [n.d.]. Trust-aware recommender systems. In RecSys ."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"J. McAuley R. Pandey and J. Leskovec. 2015. Inferring networks of substitutable and complementary products. In KDD . 785--794.  J. McAuley R. Pandey and J. Leskovec. 2015. Inferring networks of substitutable and complementary products. In KDD . 785--794.","DOI":"10.1145\/2783258.2783381"},{"key":"e_1_3_2_1_37_1","unstructured":"K. Morik P. Brockhausen and T. Joachims. 1999. Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. In ICML. 268--277.  K. Morik P. Brockhausen and T. Joachims. 1999. Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. In ICML. 268--277."},{"key":"e_1_3_2_1_38_1","volume-title":"Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. CoRR","author":"Mothilal R.K","year":"2019","unstructured":"R.K Mothilal , A Sharma , and C Tan . 2019. Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. CoRR , Vol. abs\/ 1905 .07697 ( 2019 ). R.K Mothilal, A Sharma, and C Tan. 2019. Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. CoRR , Vol. abs\/1905.07697 (2019)."},{"key":"e_1_3_2_1_39_1","unstructured":"M P O'Mahony N J Hurley and G Silvestre. 2005. Recommender systems: Attack types and strategies. In AAAI. 334--339.  M P O'Mahony N J Hurley and G Silvestre. 2005. Recommender systems: Attack types and strategies. In AAAI. 334--339."},{"key":"e_1_3_2_1_40_1","unstructured":"World Health Organization. 1978. International classification of diseases .  World Health Organization. 1978. International classification of diseases ."},{"key":"e_1_3_2_1_41_1","unstructured":"World Health Organization. 2003. Anatomical Therapeutic Chemical (ATC) Classification System with Defined Daily Doses. (2003).  World Health Organization. 2003. Anatomical Therapeutic Chemical (ATC) Classification System with Defined Daily Doses. (2003)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1031114.1031116"},{"key":"e_1_3_2_1_43_1","volume-title":"Learning Model-Agnostic Counterfactual Explanations for Tabular Data (WWW '20)","author":"Pawelczyk M","year":"2020","unstructured":"M Pawelczyk , K Broelemann , and G Kasneci . 2020 . Learning Model-Agnostic Counterfactual Explanations for Tabular Data (WWW '20) . M Pawelczyk, K Broelemann, and G Kasneci. 2020. Learning Model-Agnostic Counterfactual Explanations for Tabular Data (WWW '20)."},{"key":"e_1_3_2_1_44_1","volume-title":"The Book of Why: The New Science of Cause and Effect","author":"Pearl Judea","unstructured":"Judea Pearl and Dana Mackenzie . 2018. The Book of Why: The New Science of Cause and Effect 1 st ed.). Basic Books, Inc. , USA. Judea Pearl and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect 1st ed.). Basic Books, Inc., USA.","edition":"1"},{"key":"e_1_3_2_1_45_1","unstructured":"C Qin J Martens S Gowal D Krishnan K Dvijotham A Fawzi S De R Stanforth and P Kohli. 2019. Adversarial robustness through local linearization. In NeurIPS .  C Qin J Martens S Gowal D Krishnan K Dvijotham A Fawzi S De R Stanforth and P Kohli. 2019. Adversarial robustness through local linearization. In NeurIPS ."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-009-9123-y"},{"key":"e_1_3_2_1_47_1","volume-title":"ArXiv","volume":"2006","author":"Qin Y","year":"2020","unstructured":"Y Qin , X Wang , A Beutel , and E.H Chi . 2020 . Improving Uncertainty Estimates through the Relationship with Adversarial Robustness . ArXiv , Vol. 2006 .16375 (2020). Y Qin, X Wang, A Beutel, and E.H Chi. 2020. Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. ArXiv , Vol. 2006.16375 (2020)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"B Rastegarpanah K P Gummadi and M Crovella. 2019. Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In WWW. 231--239.  B Rastegarpanah K P Gummadi and M Crovella. 2019. Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In WWW. 231--239.","DOI":"10.1145\/3289600.3291002"},{"key":"e_1_3_2_1_49_1","volume-title":"Explaining the Predictions of Any Classifier. CoRR","author":"Ribeiro M.T","year":"2016","unstructured":"M.T Ribeiro , S Singh , and C Guestrin . 2016. \" Why Should I Trust You?\" : Explaining the Predictions of Any Classifier. CoRR , Vol. abs\/ 1602 .04938 ( 2016 ). M.T Ribeiro, S Singh, and C Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. CoRR , Vol. abs\/1602.04938 (2016)."},{"key":"e_1_3_2_1_50_1","volume-title":"Pre-training of Graph Augmented Transformers for Medication Recommendation. CoRR","author":"Shang J","year":"2019","unstructured":"J Shang , T Ma , C Xiao , and J Sun . 2019. Pre-training of Graph Augmented Transformers for Medication Recommendation. CoRR , Vol. abs\/ 1906 .00346 ( 2019 ). J Shang, T Ma, C Xiao, and J Sun. 2019. Pre-training of Graph Augmented Transformers for Medication Recommendation. CoRR , Vol. abs\/1906.00346 (2019)."},{"volume-title":"Fairness of Exposure in Rankings","author":"Singh Ashudeep","key":"e_1_3_2_1_51_1","unstructured":"Ashudeep Singh and Thorsten Joachims . 2018. Fairness of Exposure in Rankings . In KDD, Yike Guo and Faisal Farooq (Eds.). ACM , 2219--2228. Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In KDD, Yike Guo and Faisal Farooq (Eds.). ACM , 2219--2228."},{"key":"e_1_3_2_1_52_1","volume-title":"Deconvolving Feedback Loops in Recommender Systems. CoRR","author":"Sinha A","year":"2017","unstructured":"A Sinha , D. F. Gleich , and K Ramani . 2017. Deconvolving Feedback Loops in Recommender Systems. CoRR , Vol. abs\/ 1703 .01049 ( 2017 ). A Sinha, D. F. Gleich, and K Ramani. 2017. Deconvolving Feedback Loops in Recommender Systems. CoRR , Vol. abs\/1703.01049 (2017)."},{"key":"e_1_3_2_1_53_1","volume-title":"Embarrassingly Shallow Autoencoders for Sparse Data. CoRR","author":"Steck Harald","year":"2019","unstructured":"Harald Steck . 2019. Embarrassingly Shallow Autoencoders for Sparse Data. CoRR , Vol. abs\/ 1905 .03375 ( 2019 ). arxiv: 1905.03375 http:\/\/arxiv.org\/abs\/1905.03375 Harald Steck. 2019. Embarrassingly Shallow Autoencoders for Sparse Data. CoRR , Vol. abs\/1905.03375 (2019). arxiv: 1905.03375 http:\/\/arxiv.org\/abs\/1905.03375"},{"key":"e_1_3_2_1_54_1","volume-title":"The Million Song Dataset. ISMIR","author":"Bertin-Mahieux B Whitman T","year":"2011","unstructured":"B Whitman T Bertin-Mahieux , D. P.W. Ellis and P Lamere . 2011. The Million Song Dataset. ISMIR ( 2011 ). B Whitman T Bertin-Mahieux, D. P.W. Ellis and P Lamere. 2011. The Million Song Dataset. ISMIR (2011)."},{"key":"e_1_3_2_1_55_1","article-title":"Anomaly Detection Methods for Categorical Data","volume":"52","author":"Taha A","year":"2019","unstructured":"A Taha and A Hadi . 2019 . Anomaly Detection Methods for Categorical Data : A Review. ACM Comput. Surv. , Vol. 52 , 2, Article 38 (May 2019). A Taha and A Hadi. 2019. Anomaly Detection Methods for Categorical Data: A Review. ACM Comput. Surv. , Vol. 52, 2, Article 38 (May 2019).","journal-title":"A Review. ACM Comput. Surv."},{"key":"e_1_3_2_1_56_1","volume-title":"Quinn","author":"Towle Brendon","year":"2000","unstructured":"Brendon Towle and Clark N . Quinn . 2000 . Knowledge Based Recommender Systems Using Explicit User Models . Brendon Towle and Clark N. Quinn. 2000. Knowledge Based Recommender Systems Using Explicit User Models."},{"key":"e_1_3_2_1_57_1","volume-title":"Graph Attention Networks. ArXiv","author":"Velickovic P","year":"2018","unstructured":"P Velickovic , G Cucurull , A Casanova , A Romero , P Li\u00f2 , and Y Bengio . 2018. Graph Attention Networks. ArXiv , Vol. abs\/ 1710 .10903 ( 2018 ). P Velickovic, G Cucurull, A Casanova, A Romero, P Li\u00f2, and Y Bengio. 2018. Graph Attention Networks. ArXiv , Vol. abs\/1710.10903 (2018)."},{"key":"e_1_3_2_1_58_1","volume-title":"Twenty-Fourth International FLAIRS Conference .","author":"Villena-Rom\u00e1n J","year":"2011","unstructured":"J Villena-Rom\u00e1n , S Collada-P\u00e9rez , S Lana-Serrano , and J.C Gonz\u00e1lez-Crist\u00f3bal . 2011 . Hybrid approach combining machine learning and a rule-based expert system for text categorization . In Twenty-Fourth International FLAIRS Conference . J Villena-Rom\u00e1n, S Collada-P\u00e9rez, S Lana-Serrano, and J.C Gonz\u00e1lez-Crist\u00f3bal. 2011. Hybrid approach combining machine learning and a rule-based expert system for text categorization. In Twenty-Fourth International FLAIRS Conference ."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"J Wang and J Caverlee. 2019. Recurrent recommendation with local coherence (WSDM).  J Wang and J Caverlee. 2019. Recurrent recommendation with local coherence (WSDM).","DOI":"10.1145\/3289600.3291024"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Z Wang Z Jiang Z Ren J Tang and D Yin. 2018. A path-constrained framework for discriminating substitutable and complementary products in e-commerce. In WWW .  Z Wang Z Jiang Z Ren J Tang and D Yin. 2018. A path-constrained framework for discriminating substitutable and complementary products in e-commerce. In WWW .","DOI":"10.1145\/3159652.3159710"},{"key":"e_1_3_2_1_61_1","volume-title":"BMC Health Serv Res. 2008","author":"Filzmoser P","year":"2008","unstructured":"Filzmoser P Gyimesi M. Weisser A, Endel G. 2008. ATC -> ICD -- evaluating the reliability of prognoses for ICD-10 diagnoses derived from the ATC-Code of prescriptions. BMC Health Serv Res. 2008 ( 2008 ). Filzmoser P Gyimesi M. Weisser A, Endel G. 2008. ATC -> ICD -- evaluating the reliability of prognoses for ICD-10 diagnoses derived from the ATC-Code of prescriptions. BMC Health Serv Res. 2008 (2008)."},{"key":"e_1_3_2_1_62_1","article-title":"Explaining `explaining away'. Pattern Analysis and Machine Intelligence","volume":"15","author":"Wellman Michael","year":"1993","unstructured":"Michael Wellman and Max Henrion . 1993 . Explaining `explaining away'. Pattern Analysis and Machine Intelligence , IEEE Transactions on , Vol. 15 (04 1993), 287--292. Michael Wellman and Max Henrion. 1993. Explaining `explaining away'. Pattern Analysis and Machine Intelligence, IEEE Transactions on , Vol. 15 (04 1993), 287--292.","journal-title":"IEEE Transactions on"},{"key":"e_1_3_2_1_63_1","unstructured":"A. B. Wilcox and G. Hripcsak. 2003. The role of domain knowledge in automating medical text report classification. JAMIA (2003).  A. B. Wilcox and G. Hripcsak. 2003. The role of domain knowledge in automating medical text report classification. JAMIA (2003)."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"crossref","unstructured":"C Xie Y Wu L Maaten A Yuille and K He. 2019. Feature denoising for improving adversarial robustness. In ICCV .  C Xie Y Wu L Maaten A Yuille and K He. 2019. Feature denoising for improving adversarial robustness. In ICCV .","DOI":"10.1109\/CVPR.2019.00059"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109911"},{"key":"e_1_3_2_1_66_1","volume-title":"Theoretically Principled Trade-off between Robustness and Accuracy","author":"Zhang H","year":"2019","unstructured":"H Zhang , Y Yu , J Jiao , E Xing , L Ghaoui , and M Jordan . 2019. Theoretically Principled Trade-off between Robustness and Accuracy . , Vol. CoRR\/ 1901 .08573 ( 2019 ). H Zhang, Y Yu, J Jiao, E Xing, L Ghaoui, and M Jordan. 2019. Theoretically Principled Trade-off between Robustness and Accuracy. , Vol. CoRR\/1901.08573 (2019)."},{"key":"e_1_3_2_1_67_1","first-page":"1372","article-title":"Deepvoting: A robust and explainable deep network for semantic part detection under partial occlusion","volume":"2018","author":"Zhang Z","year":"2018","unstructured":"Z Zhang , C Xie , J Wang , L Xie , and A Yuille . 2018 . Deepvoting: A robust and explainable deep network for semantic part detection under partial occlusion . In ICCV , 2018. 1372 -- 1380 . Z Zhang, C Xie, J Wang, L Xie, and A Yuille. 2018. Deepvoting: A robust and explainable deep network for semantic part detection under partial occlusion. In ICCV, 2018. 1372--1380.","journal-title":"ICCV"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"crossref","unstructured":"Q Zhao J Chen M Chen S Jain A Beutel F Belletti and E. Chi. 2018. Categorical-Attributes-Based Item Classification for Recommender Systems (RecSys '18).  Q Zhao J Chen M Chen S Jain A Beutel F Belletti and E. Chi. 2018. Categorical-Attributes-Based Item Classification for Recommender Systems (RecSys '18).","DOI":"10.1145\/3240323.3240367"},{"key":"e_1_3_2_1_69_1","volume-title":"Deep Interest Network for Click-Through Rate Prediction (ACM SIGKDD '18)","author":"Zhou G","year":"2018","unstructured":"G Zhou , X Zhu , C Song , Y Fan , H Zhu , X Ma , Y Yan , J Jin , H Li , and K Gai . 2018 . Deep Interest Network for Click-Through Rate Prediction (ACM SIGKDD '18) . G Zhou, X Zhu, C Song, Y Fan, H Zhu, X Ma, Y Yan, J Jin, H Li, and K Gai. 2018. Deep Interest Network for Click-Through Rate Prediction (ACM SIGKDD '18)."},{"key":"e_1_3_2_1_70_1","volume-title":"Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571","author":"Zmigrod R","year":"2019","unstructured":"R Zmigrod , S Mielke , H Wallach , and R Cotterell . 2019. Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 ( 2019 ). R Zmigrod, S Mielke, H Wallach, and R Cotterell. 2019. Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 (2019)."}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Israel","acronym":"WSDM '21"},"container-title":["Proceedings of the 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441784","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441784","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:35Z","timestamp":1750193255000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441784"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":70,"alternative-id":["10.1145\/3437963.3441784","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441784","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}