{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:51:37Z","timestamp":1767340297400,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"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":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615223","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"3803-3807","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7076-5779","authenticated-orcid":false,"given":"Wenzhuo","family":"Cheng","sequence":"first","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0562-1987","authenticated-orcid":false,"given":"Ke","family":"Ding","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2523-9971","authenticated-orcid":false,"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5390-2655","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7744-7789","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6682-1448","authenticated-orcid":false,"given":"Linjian","family":"Mo","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Tetsuo Asano and Tadashi Matsumoto. 2008. ENTROPY RELATIVE ENTROPY AND MUTUAL INFORMATION.  Tetsuo Asano and Tadashi Matsumoto. 2008. ENTROPY RELATIVE ENTROPY AND MUTUAL INFORMATION."},{"key":"e_1_3_2_1_2_1","volume-title":"Courville","author":"Belghazi Ishmael","year":"2018","unstructured":"Ishmael Belghazi , Sai Rajeswar , Aristide Baratin , R. Devon Hjelm , and Aaron C . Courville . 2018 . MINE : Mutual Information Neural Estimation. ArXiv , Vol. abs\/ 1801 .04062 (2018). Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, and Aaron C. Courville. 2018. MINE: Mutual Information Neural Estimation. ArXiv, Vol. abs\/1801.04062 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. ArXiv","author":"Chang Jianxin","year":"2023","unstructured":"Jianxin Chang , Chenbin Zhang , Yiqun Hui , Dewei Leng , Yanan Niu , Yang Song , and Kun Gai . 2023. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. ArXiv , Vol. abs\/ 2302 .01115 ( 2023 ). Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. 2023. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. ArXiv, Vol. abs\/2302.01115 (2023)."},{"key":"e_1_3_2_1_4_1","unstructured":"Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever and P. Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In NIPS.  Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever and P. Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In NIPS."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"e_1_3_2_1_7_1","volume-title":"Learning deep representations by mutual information estimation and maximization. ArXiv","author":"Hjelm R. Devon","year":"2018","unstructured":"R. Devon Hjelm , Alex Fedorov , Samuel Lavoie-Marchildon , Karan Grewal , Adam Trischler , and Yoshua Bengio . 2018. Learning deep representations by mutual information estimation and maximization. ArXiv , Vol. abs\/ 1808 .06670 ( 2018 ). R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. ArXiv, Vol. abs\/1808.06670 (2018)."},{"key":"e_1_3_2_1_8_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. ArXiv","author":"Hu Edward J.","year":"2021","unstructured":"Edward J. Hu , Yelong Shen , Phillip Wallis , Zeyuan Allen-Zhu , Yuanzhi Li , Shean Wang , and Weizhu Chen . 2021. LoRA: Low-Rank Adaptation of Large Language Models. ArXiv , Vol. abs\/ 2106 .09685 ( 2021 ). Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. 2021. LoRA: Low-Rank Adaptation of Large Language Models. ArXiv, Vol. abs\/2106.09685 (2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/07468342.1996.11973744"},{"key":"e_1_3_2_1_10_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization. CoRR , Vol. abs\/ 1412 .6980 (2014). Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR, Vol. abs\/1412.6980 (2014)."},{"key":"e_1_3_2_1_11_1","unstructured":"Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NIPS.  Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NIPS."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481941"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412236"},{"key":"e_1_3_2_1_14_1","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey E. Hinton . 2008 . Visualizing Data using t-SNE . Journal of Machine Learning Research , Vol. 9 (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey E. Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research, Vol. 9 (2008), 2579--2605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_15_1","volume-title":"Coordinating Filters for Faster Deep Neural Networks. 2017 IEEE International Conference on Computer Vision (ICCV)","author":"Wen Wei","year":"2017","unstructured":"Wei Wen , Cong Xu , Chunpeng Wu , Yandan Wang , Yiran Chen , and Hai Helen Li . 2017 . Coordinating Filters for Faster Deep Neural Networks. 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 658--666. Wei Wen, Cong Xu, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Helen Li. 2017. Coordinating Filters for Faster Deep Neural Networks. 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 658--666."},{"key":"e_1_3_2_1_16_1","volume-title":"Apg: Adaptive parameter generation network for click-through rate prediction. arXiv preprint arXiv:2203.16218","author":"Yan Bencheng","year":"2022","unstructured":"Bencheng Yan , Pengjie Wang , Kai Zhang , Feng Li , Jian Xu , and Bo Zheng . 2022 . Apg: Adaptive parameter generation network for click-through rate prediction. arXiv preprint arXiv:2203.16218 (2022). Bencheng Yan, Pengjie Wang, Kai Zhang, Feng Li, Jian Xu, and Bo Zheng. 2022. Apg: Adaptive parameter generation network for click-through rate prediction. arXiv preprint arXiv:2203.16218 (2022)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557541"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Birmingham United Kingdom","acronym":"CIKM '23"},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615223","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:58Z","timestamp":1750178218000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":17,"alternative-id":["10.1145\/3583780.3615223","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615223","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}