{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:33:27Z","timestamp":1784738007336,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":38,"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:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467304","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:03Z","timestamp":1628748723000},"page":"840-850","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["Learning to Embed Categorical Features without Embedding Tables for Recommendation"],"prefix":"10.1145","author":[{"given":"Wang-Cheng","family":"Kang","sequence":"first","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek Zhiyuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiansheng","family":"Yao","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyang","family":"Yi","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichan","family":"Hong","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ed H.","family":"Chi","sequence":"additional","affiliation":[{"name":"Google Research, Brain Team, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Raman Arora Amitabh Basu Poorya Mianjy and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In ICLR.  Raman Arora Amitabh Basu Poorya Mianjy and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In ICLR."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/362686.362692"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177706645"},{"key":"e_1_3_2_1_4_1","volume-title":"Wegman","author":"Carter Larry","year":"1977","unstructured":"Larry Carter and Mark N . Wegman . 1977 . Universal Classes of Hash Functions (Extended Abstract). In STOC. Larry Carter and Mark N. Wegman. 1977. Universal Classes of Hash Functions (Extended Abstract). In STOC."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. ACM 191--198.  Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. ACM 191--198.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_6_1","volume-title":"Mirrokni","author":"Datar Mayur","year":"2004","unstructured":"Mayur Datar , Nicole Immorlica , Piotr Indyk , and Vahab S . Mirrokni . 2004 . Locality-sensitive hashing scheme based on p-stable distributions. In SoCG. ACM , 253--262. Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In SoCG. ACM, 253--262."},{"key":"e_1_3_2_1_7_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In NAACL-HLT. Association for Computational Linguistics . Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. Association for Computational Linguistics."},{"key":"e_1_3_2_1_8_1","unstructured":"Jonathan Frankle and Michael Carbin. 2019. The Lottery Ticket Hypothesis: Finding Sparse Trainable Neural Networks. In ICLR. OpenReview.net.  Jonathan Frankle and Michael Carbin. 2019. The Lottery Ticket Hypothesis: Finding Sparse Trainable Neural Networks. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_9_1","unstructured":"Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron C. Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS.  Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron C. Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS."},{"key":"e_1_3_2_1_10_1","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. ijcai.org.  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI. ijcai.org."},{"key":"e_1_3_2_1_11_1","article-title":"The MovieLens Datasets","volume":"5","author":"Maxwell Harper F.","year":"2016","unstructured":"F. Maxwell Harper and Joseph A. Konstan . 2016 . The MovieLens Datasets : History and Context. ACM Trans. Interact. Intell. Syst. , Vol. 5 , 4 (2016), 19:1--19:19. F. Maxwell Harper and Joseph A. Konstan. 2016. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst., Vol. 5, 4 (2016), 19:1--19:19.","journal-title":"History and Context. ACM Trans. Interact. Intell. Syst."},{"key":"e_1_3_2_1_12_1","volume-title":"Deep Residual Learning for Image Recognition","author":"He Kaiming","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016. Deep Residual Learning for Image Recognition . In CVPR. IEEE Computer Society , 770--778. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. IEEE Computer Society, 770--778."},{"key":"e_1_3_2_1_13_1","volume-title":"McAuley","author":"He Ruining","year":"2017","unstructured":"Ruining He , Wang-Cheng Kang , and Julian J . McAuley . 2017 a. Translation-based Recommendation. In RecSys. ACM. Ruining He, Wang-Cheng Kang, and Julian J. McAuley. 2017a. Translation-based Recommendation. In RecSys. ACM."},{"key":"e_1_3_2_1_14_1","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In WWW. ACM.  Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In WWW. ACM."},{"key":"e_1_3_2_1_15_1","volume-title":"Weinberger","author":"Huang Gao","year":"2016","unstructured":"Gao Huang , Zhuang Liu , and Kilian Q . Weinberger . 2016 . Densely Connected Convolutional Networks. CoRR , Vol. abs\/ 1608 .06993 (2016). arxiv: 1608.06993 Gao Huang, Zhuang Liu, and Kilian Q. Weinberger. 2016. Densely Connected Convolutional Networks. CoRR, Vol. abs\/1608.06993 (2016). arxiv: 1608.06993"},{"key":"e_1_3_2_1_16_1","volume-title":"ICML (JMLR Workshop and Conference Proceedings","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . In ICML (JMLR Workshop and Conference Proceedings , Vol. 37). JMLR.org. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML (JMLR Workshop and Conference Proceedings, Vol. 37). JMLR.org."},{"key":"e_1_3_2_1_17_1","volume-title":"Le","author":"Joglekar Manas R.","year":"2020","unstructured":"Manas R. Joglekar , Cong Li , Mei Chen , Taibai Xu , Xiaoming Wang , Jay K. Adams , Pranav Khaitan , Jiahui Liu , and Quoc V . Le . 2020 . Neural Input Search for Large Scale Recommendation Models. In SIGKDD. ACM. Manas R. Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, and Quoc V. Le. 2020. Neural Input Search for Large Scale Recommendation Models. In SIGKDD. ACM."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Wang-Cheng Kang and Julian John McAuley. 2019. Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation. In CIKM. ACM.  Wang-Cheng Kang and Julian John McAuley. 2019. Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation. In CIKM. ACM.","DOI":"10.1145\/3357384.3357930"},{"key":"e_1_3_2_1_19_1","volume-title":"PRADO: Projection Attention Networks for Document Classification On-Device","author":"Krishnamoorthi Karthik","year":"2019","unstructured":"Karthik Krishnamoorthi , Sujith Ravi , and Zornitsa Kozareva . 2019 . PRADO: Projection Attention Networks for Document Classification On-Device . In EMNLP-IJCNLP. Association for Computational Linguistics , 5011--5020. Karthik Krishnamoorthi, Sujith Ravi, and Zornitsa Kozareva. 2019. PRADO: Projection Attention Networks for Document Classification On-Device. In EMNLP-IJCNLP. Association for Computational Linguistics, 5011--5020."},{"key":"e_1_3_2_1_20_1","volume-title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net.","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan , Mingda Chen , Sebastian Goodman , Kevin Gimpel , Piyush Sharma , and Radu Soricut . 2020 . ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_21_1","unstructured":"Shiyu Liang and R. Srikant. 2017. Why Deep Neural Networks for Function Approximation?. In ICLR. OpenReview.net.  Shiyu Liang and R. Srikant. 2017. Why Deep Neural Networks for Function Approximation?. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_22_1","unstructured":"Siyi Liu Chen Gao Yihong Chen Depeng Jin and Yong Li. 2020. Learnable Embedding Sizes for Recommender Systems. In ICLR.  Siyi Liu Chen Gao Yihong Chen Depeng Jin and Yong Li. 2020. Learnable Embedding Sizes for Recommender Systems. In ICLR."},{"key":"e_1_3_2_1_23_1","unstructured":"Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu and Liwei Wang. 2017. The Expressive Power of Neural Networks: A View from the Width. In NIPS.  Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu and Liwei Wang. 2017. The Expressive Power of Neural Networks: A View from the Width. In NIPS."},{"key":"e_1_3_2_1_24_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Gregory S. Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS.  Tomas Mikolov Ilya Sutskever Kai Chen Gregory S. Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS."},{"key":"e_1_3_2_1_25_1","volume-title":"Mish: A Self Regularized Non-Monotonic Neural Activation Function. In BMVC.","author":"Misra Diganta","year":"2010","unstructured":"Diganta Misra . 2010 . Mish: A Self Regularized Non-Monotonic Neural Activation Function. In BMVC. Diganta Misra. 2010. Mish: A Self Regularized Non-Monotonic Neural Activation Function. In BMVC."},{"key":"e_1_3_2_1_26_1","volume-title":"McAuley","author":"Ni Jianmo","year":"2019","unstructured":"Jianmo Ni , Jiacheng Li , and Julian J . McAuley . 2019 . Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP-IJCNLP. Association for Computational Linguistics . Jianmo Ni, Jiacheng Li, and Julian J. McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP-IJCNLP. Association for Computational Linguistics."},{"key":"e_1_3_2_1_27_1","unstructured":"Sujith Ravi. 2019. Efficient On-Device Models using Neural Projections. In ICML.  Sujith Ravi. 2019. Efficient On-Device Models using Neural Projections. In ICML."},{"key":"e_1_3_2_1_28_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_1_29_1","doi-asserted-by":"crossref","unstructured":"Rico Sennrich Barry Haddow and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. In ACL.  Rico Sennrich Barry Haddow and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. In ACL.","DOI":"10.18653\/v1\/P16-1162"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Joan Serr\u00e0 and Alexandros Karatzoglou. 2017. Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input\/Output Networks. In RecSys. ACM.  Joan Serr\u00e0 and Alexandros Karatzoglou. 2017. Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input\/Output Networks. In RecSys. ACM.","DOI":"10.1145\/3109859.3109876"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"e_1_3_2_1_32_1","unstructured":"Hao-Jun Michael Shi Dheevatsa Mudigere Maxim Naumov and Jiyan Yang. 2020. Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. In SIGKDD.  Hao-Jun Michael Shi Dheevatsa Mudigere Maxim Naumov and Jiyan Yang. 2020. Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems. In SIGKDD."},{"key":"e_1_3_2_1_33_1","volume-title":"Implicit Neural Representations with Periodic Activation Functions. CoRR","author":"Sitzmann Vincent","year":"2020","unstructured":"Vincent Sitzmann , Julien N. P. Martel , Alexander W. Bergman , David B. Lindell , and Gordon Wetzstein . 2020. Implicit Neural Representations with Periodic Activation Functions. CoRR , Vol. abs\/ 2006 .09661 ( 2020 ). arxiv: 2006.09661 Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. 2020. Implicit Neural Representations with Periodic Activation Functions. CoRR, Vol. abs\/2006.09661 (2020). arxiv: 2006.09661"},{"key":"e_1_3_2_1_34_1","volume-title":"Jonas Meinertz Hansen, and Ole Winther","author":"Svenstrup Dan","year":"2017","unstructured":"Dan Svenstrup , Jonas Meinertz Hansen, and Ole Winther . 2017 . Hash Embeddings for Efficient Word Representations. In NIPS. Dan Svenstrup, Jonas Meinertz Hansen, and Ole Winther. 2017. Hash Embeddings for Efficient Word Representations. In NIPS."},{"key":"e_1_3_2_1_35_1","unstructured":"Matthew Tancik Pratul P. Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan T. Barron and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In NeurIPS.  Matthew Tancik Pratul P. Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan T. Barron and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In NeurIPS."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Kilian Q. Weinberger Anirban Dasgupta John Langford Alexander J. Smola and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. In ICML.  Kilian Q. Weinberger Anirban Dasgupta John Langford Alexander J. Smola and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. In ICML.","DOI":"10.1145\/1553374.1553516"},{"key":"e_1_3_2_1_37_1","volume-title":"Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi.","author":"Yi Xinyang","year":"2019","unstructured":"Xinyang Yi , Ji Yang , Lichan Hong , Derek Zhiyuan Cheng , Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi. 2019 . Sampling-bias-corrected neural modeling for large corpus item recommendations. In RecSys. ACM. Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In RecSys. ACM."},{"key":"e_1_3_2_1_38_1","volume-title":"Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, and Wenzhe Shi.","author":"Zhang Caojin","year":"2020","unstructured":"Caojin Zhang , Yicun Liu , Yuanpu Xie , Sofia Ira Ktena , Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, and Wenzhe Shi. 2020 . Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. In RecSys . Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, and Wenzhe Shi. 2020. Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems. In RecSys."}],"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 & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467304","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:22Z","timestamp":1750191502000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":38,"alternative-id":["10.1145\/3447548.3467304","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467304","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"}}]}}