{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:23:40Z","timestamp":1792819420737,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"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":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403091","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:18:56Z","timestamp":1597965536000},"page":"483-491","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":200,"title":["Disentangled Self-Supervision in Sequential Recommenders"],"prefix":"10.1145","author":[{"given":"Jianxin","family":"Ma","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University & Key Laboratory of Pervasive Computing, Ministry of Education, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University & Key Laboratory of Pervasive Computing, Ministry of Education, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Representation learning: A review and new perspectives","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE transactions on pattern analysis and machine intelligence, Vol. 35 , 8 ( 2013 ), 1798--1828. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11867"},{"key":"e_1_3_2_1_3_1","volume-title":"Understanding disentangling in $beta $-VAE. arXiv preprint arXiv:1804.03599","author":"Burgess Christopher P","year":"2018","unstructured":"Christopher P Burgess , Irina Higgins , Arka Pal , Loic Matthey , Nick Watters , Guillaume Desjardins , and Alexander Lerchner . 2018. Understanding disentangling in $beta $-VAE. arXiv preprint arXiv:1804.03599 ( 2018 ). Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner. 2018. Understanding disentangling in $beta $-VAE. arXiv preprint arXiv:1804.03599 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290999"},{"key":"e_1_3_2_1_5_1","unstructured":"Tian Qi Chen Xuechen Li Roger B Grosse and David K Duvenaud. 2018a. Isolating sources of disentanglement in variational autoencoders. In Advances in Neural Information Processing Systems. 2610--2620.  Tian Qi Chen Xuechen Li Roger B Grosse and David K Duvenaud. 2018a. Isolating sources of disentanglement in variational autoencoders. In Advances in Neural Information Processing Systems. 2610--2620."},{"key":"e_1_3_2_1_6_1","volume-title":"NIPS","author":"Chen Xi","year":"2016","unstructured":"Xi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , and Pieter Abbeel . 2016 . Infogan: Interpretable representation learning by information maximizing generative adversarial nets . In NIPS 2016. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS 2016."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159668"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963776"},{"key":"e_1_3_2_1_10_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_11_1","volume-title":"Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan.","author":"Dilokthanakul Nat","year":"2016","unstructured":"Nat Dilokthanakul , Pedro AM Mediano , Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan. 2016 . Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648 (2016). Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan. 2016. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648 (2016)."},{"key":"e_1_3_2_1_12_1","unstructured":"Emilien Dupont. 2018. Learning disentangled joint continuous and discrete representations. In Advances in Neural Information Processing Systems. 710--720.  Emilien Dupont. 2018. Learning disentangled joint continuous and discrete representations. In Advances in Neural Information Processing Systems. 710--720."},{"key":"e_1_3_2_1_13_1","volume-title":"Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722","author":"He Kaiming","year":"2019","unstructured":"Kaiming He , Haoqi Fan , Yuxin Wu , Saining Xie , and Ross Girshick . 2019. Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722 ( 2019 ). Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2019. Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722 (2019)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959152"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109882"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0030"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_19_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi , Alexandros Karatzoglou , Linas Baltrunas , and Domonkos Tikk . 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 ( 2015 ). Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Learning Representations","volume":"3","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins , Loic Matthey , Arka Pal , Christopher Burgess , Xavier Glorot , Matthew Botvinick , Shakir Mohamed , and Alexander Lerchner . 2017 . beta-vae: Learning basic visual concepts with a constrained variational framework . In International Conference on Learning Representations , Vol. 3 . Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations, Vol. 3."},{"key":"e_1_3_2_1_21_1","volume-title":"Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670","author":"Hjelm R Devon","year":"2018","unstructured":"R Devon Hjelm , Alex Fedorov , Samuel Lavoie-Marchildon , Karan Grewal , Phil Bachman , Adam Trischler , and Yoshua Bengio . 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 ( 2018 ). R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210017"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/273"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Machine Learning. 2654--2663","author":"Kim Hyunjik","year":"2018","unstructured":"Hyunjik Kim and Andriy Mnih . 2018 . Disentangling by Factorising . In International Conference on Machine Learning. 2654--2663 . Hyunjik Kim and Andriy Mnih. 2018. Disentangling by Factorising. In International Conference on Machine Learning. 2654--2663."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference for Learning Representations.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization . In International Conference for Learning Representations. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In International Conference for Learning Representations."},{"key":"e_1_3_2_1_28_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132926"},{"key":"e_1_3_2_1_31_1","volume-title":"Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557","author":"Li Liunian Harold","year":"2019","unstructured":"Liunian Harold Li , Mark Yatskar , Da Yin , Cho-Jui Hsieh , and Kai-Wei Chang . 2019 . Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557 (2019). Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019. Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557 (2019)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098077"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of SIGKDD","author":"Liu Ninghao","year":"2019","unstructured":"Ninghao Liu , Qiaoyu Tan , Yuening Li , Hongxia Yang , Jingren Zhou , and Xia Hu . 2019 . Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding . In Proceedings of SIGKDD 2019. Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, and Xia Hu. 2019. Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding. In Proceedings of SIGKDD 2019."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0135"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219950"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma , Peng Cui , Kun Kuang , Xin Wang , and Wenwu Zhu . 2019 a. Disentangled Graph Convolutional Networks . In Proceedings of the 36th International Conference on Machine Learning (ICML 2019). Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019 a. Disentangled Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019)."},{"key":"e_1_3_2_1_38_1","unstructured":"Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019 b. Learning disentangled representations for recommendation. In Advances in Neural Information Processing Systems. 5712--5723.  Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019 b. Learning disentangled representations for recommendation. In Advances in Neural Information Processing Systems. 5712--5723."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_32"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"e_1_3_2_1_41_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_42_1","unstructured":"Steffen Rendle. 2019. Evaluation Metrics for Item Recommendation under Sampling.  Steffen Rendle. 2019. Evaluation Metrics for Item Recommendation under Sampling."},{"key":"e_1_3_2_1_43_1","volume-title":"Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence.","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 Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/192844.192905"},{"key":"e_1_3_2_1_46_1","first-page":"1","volume-title":"NIPS","volume":"20","author":"Salakhutdinov Ruslan","year":"2011","unstructured":"Ruslan Salakhutdinov and Andriy Mnih . 2011 . Probabilistic matrix factorization . In NIPS , Vol. 20 . 1 - 8 . Ruslan Salakhutdinov and Andriy Mnih. 2011. Probabilistic matrix factorization. In NIPS, Vol. 20. 1-8."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_1_48_1","unstructured":"Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. In Advances in Neural Information Processing Systems. 1857?-1865.  Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. In Advances in Neural Information Processing Systems. 1857?-1865."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_51_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998-6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998-6008."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767694"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018689"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914683"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11618"},{"key":"e_1_3_2_1_59_1","volume-title":"Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. arXiv: arXiv","author":"Zhou Chang","year":"2005","unstructured":"Chang Zhou , Jianxin Ma , Jianwei Zhang , Jingren Zhou , and Hongxia Yang . 2020. Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. arXiv: arXiv : 2005 .12964 Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, and Hongxia Yang. 2020. Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. arXiv: arXiv: 2005.12964"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403091","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403091","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:38Z","timestamp":1750200098000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":59,"alternative-id":["10.1145\/3394486.3403091","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403091","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}