{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T20:54:05Z","timestamp":1790715245219,"version":"4.1.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62036005"],"award-info":[{"award-number":["62036005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused by historical preferences of uploaders. Since historical preferences are not fully reliable and may reflect obsolete behaviors, over-reliance on them should be avoided as knowledge and interests dynamically evolve. In this article, we propose a Deconfounded Cross-Modal matching model to mitigate such bias. Specifically, uploaders\u2019 personal preferences of music genres are identified as confounders that spuriously correlate music embeddings and background music selections, causing the learned system to over-recommend music from majority groups. To resolve such confounders, backdoor adjustment is utilized to deconfound the spurious correlation between music embeddings and prediction scores. We further utilize Monte Carlo estimator with batch-level average as the approximations to avoid integrating the entire confounder space calculated by the adjustment. Furthermore, we design a teacher\u2013student network to utilize the matching of music videos, which is professionally generated content (PGC) with specialized matching, to better recommend content-matching background music. The PGC data are modeled by a teacher network to guide the matching of uploader-selected UGC data of student network by Kullback\u2013Leibler\u2013based knowledge transfer. Extensive experiments on the TT-150k-genre dataset demonstrate the effectiveness of the proposed method. The code is publicly available on\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/jing-1\/DecCM\">https:\/\/github.com\/jing-1\/DecCM<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3650042","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T12:06:20Z","timestamp":1709726780000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deconfounded Cross-modal Matching for Content-based Micro-video Background Music Recommendation"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-1500","authenticated-orcid":false,"given":"Jing","family":"Yi","sequence":"first","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-1066","authenticated-orcid":false,"given":"Zhenzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Some new estimates of the \u2018Jensen gap\u2019","volume":"1","author":"Abramovich Shoshana","year":"2016","unstructured":"Shoshana Abramovich and Lars-Erik Persson. 2016. Some new estimates of the \u2018Jensen gap\u2019. J. Inequal. Appl. 1 (2016), 1\u20139.","journal-title":"J. Inequal. Appl."},{"key":"e_1_3_2_3_2","article-title":"YouTube-8M: A large-scale video classification benchmark","author":"Abu-El-Haija Sami","year":"2016","unstructured":"Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. YouTube-8M: A large-scale video classification benchmark. arXiv:1609.08675. Retrieved from https:\/\/arxiv.org\/abs\/1609.08675","journal-title":"arXiv:1609.08675"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2801130"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1145\/3292500.3330745","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Beutel Alex","year":"2019","unstructured":"Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, et\u00a0al. 2019. Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2212\u20132220."},{"key":"e_1_3_2_6_2","unstructured":"Jiansong Chao HaofenWang Wenlei Zhou Weinan Zhang and Yong Yu. 2011. Tunesensor: A semantic-driven music recommendation service for digital photo albums. In Proceedings of the International Semantic Web Conference. 353\u2013361."},{"key":"e_1_3_2_7_2","first-page":"21","volume-title":"Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chen Jiawei","year":"2021","unstructured":"Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021. AutoDebias: Learning to debias for recommendation. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 21\u201330."},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2022. Bias and debias in recom-mender system: A survey and future directions. ACM Trans. Inf. Syst. 41 3 (2022). 1\u201339.","DOI":"10.1145\/3564284"},{"key":"e_1_3_2_9_2","first-page":"2037","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Di Shangzhe","year":"2021","unstructured":"Shangzhe Di, Zeren Jiang, Si Liu, Zhaokai Wang, Leyan Zhu, Zexin He, Hongming Liu, and Shuicheng Yan. 2021. Video background music generation with controllable music transformer. In Proceedings of the ACM International Conference on Multimedia. 2037\u20132045."},{"key":"e_1_3_2_10_2","unstructured":"John Duchi Elad Hazan and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 7 (2011). 2121\u20132159."},{"key":"e_1_3_2_11_2","first-page":"1459","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Eyben Florian","year":"2010","unstructured":"Florian Eyben, Martin W\u00f6llmer, and Bj\u00f6rn Schuller. 2010. Opensmile: The munich versatile and fast open-source audio feature extractor. In Proceedings of the ACM International Conference on Multimedia. 1459\u20131462."},{"key":"e_1_3_2_12_2","first-page":"1","volume-title":"Proceedings of the International Conference on Contemporary Computing.","author":"Fulzele Prasenjeet","year":"2018","unstructured":"Prasenjeet Fulzele, Rajat Singh, Naman Kaushik, and Kavita Pandey. 2018. A hybrid model for music genre classification using LSTM and SVM. In Proceedings of the International Conference on Contemporary Computing.1\u20133."},{"key":"e_1_3_2_13_2","volume-title":"Causal Inference in Statistics: A Primer","author":"Glymour Madelyn","year":"2016","unstructured":"Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. John Wiley & Sons."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Xiangnan He Yang Zhang Fuli Feng Chonggang Song Lingling Yi Guohui Ling and Yongdong Zhang. 2022. Addressing confounding feature issue for causal recommendation. ACM Trans. Inf. Syst. 41 3 (2022). 1\u201323.","DOI":"10.1145\/3559757"},{"key":"e_1_3_2_16_2","first-page":"131","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing.","author":"Hershey Shawn","year":"2017","unstructured":"Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, et\u00a0al. 2017. CNN architectures for large-scale audio classification. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing.131\u2013135."},{"key":"e_1_3_2_17_2","first-page":"353","volume-title":"Proceedings of the ACM International Conference on Multimedia Retrieval","author":"Hong Sungeun","year":"2018","unstructured":"Sungeun Hong, Woobin Im, and Hyun S. Yang. 2018. CBVMR: Content-based video-music retrieval using soft intra-modal structure constraint. In Proceedings of the ACM International Conference on Multimedia Retrieval. 353\u2013361."},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_19_2","first-page":"1647","volume-title":"Proceedings of the Annual International ACM SIGIR Conference Research and Development in Information Retrieval","author":"Lee Jae-woong","year":"2021","unstructured":"Jae-woong Lee, Seongmin Park, and Jongwuk Lee. 2021. Dual unbiased recommender learning for implicit feedback. In Proceedings of the Annual International ACM SIGIR Conference Research and Development in Information Retrieval. 1647\u20131651."},{"key":"e_1_3_2_20_2","first-page":"604","volume-title":"International Society for Music Information Retrieval Conference.","author":"Li Bochen","year":"2019","unstructured":"Bochen Li and Aparna Kumar. 2019. Query by video: Cross-modal music retrieval. In International Society for Music Information Retrieval Conference.604\u2013611."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3495163"},{"issue":"1","key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3533725","article-title":"Be causal: De-biasing social network confounding in recommendation","volume":"17","author":"Li Qian","year":"2023","unstructured":"Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2023. Be causal: De-biasing social network confounding in recommendation. ACM Trans. Knowl. Discov. Data 17, 1 (2023), 1\u201323.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the Uncertainty Artificial Intelligence Workshop","author":"Liang Dawen","year":"2016","unstructured":"Dawen Liang, Laurent Charlin, and David M. Blei. 2016. Causal inference for recommendation. In Proceedings of the Uncertainty Artificial Intelligence Workshop."},{"key":"e_1_3_2_24_2","first-page":"899","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Lin Jen-Chun","year":"2015","unstructured":"Jen-Chun Lin, Wen-Li Wei, and Hsin-Min Wang. 2015. EMV-matchmaker: Emotional temporal course modeling and matching for automatic music video generation. In Proceedings of the ACM International Conference on Multimedia. 899\u2013902."},{"key":"e_1_3_2_25_2","first-page":"372","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Lin Jen-Chun","year":"2016","unstructured":"Jen-Chun Lin, Wen-Li Wei, and Hsin-Min Wang. 2016. Automatic music video generation based on emotion-oriented pseudo song prediction and matching. In Proceedings of the ACM International Conference on Multimedia. 372\u2013376."},{"key":"e_1_3_2_26_2","first-page":"2772","volume-title":"IEEE International Conference on Acoustics, Speech, and Signal Processing.","author":"Lin Jen-Chun","year":"2016","unstructured":"Jen-Chun Lin, Wen-Li Wei, and Hsin-Min Wang. 2016. DEMV-matchmaker: Emotional temporal course representation and deep similarity matching for automatic music video generation. In IEEE International Conference on Acoustics, Speech, and Signal Processing.2772\u20132776."},{"key":"e_1_3_2_27_2","first-page":"519","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Lin Jen-Chun","year":"2017","unstructured":"Jen-Chun Lin, Wen-Li Wei, James Yang, Hsin-Min Wang, and Hong-Yuan Mark Liao. 2017. Automatic music video generation based on simultaneous soundtrack recommendation and video editing. In Proceedings of the ACM International Conference on Multimedia. 519\u2013527."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.07.001"},{"key":"e_1_3_2_29_2","first-page":"831","volume-title":"Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Liu Dugang","year":"2020","unstructured":"Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2020. A general knowledge distillation framework for counterfactual recommendation via uniform data. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 831\u2013840."},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Zhongzhou Liu Yuan Fang and Min Wu. 2022. Mitigating popularity bias for users and items with fairness-centric adaptive recommendation. ACM Trans. Inf. Syst. 41 3 (2022). 1\u201327.","DOI":"10.1145\/3564286"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1949.10483310"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3152598"},{"issue":"50","key":"e_1_3_2_33_2","article-title":"Link recommendation algorithms and dynamics of polarization in online social networks","volume":"118","author":"Santos Fernando P.","year":"2021","unstructured":"Fernando P. Santos, Yphtach Lelkes, and Simon A. Levin. 2021. Link recommendation algorithms and dynamics of polarization in online social networks. Proc. Natl. Acad. Sci. U.S.A. 118, 50 (2021).","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/978-3-319-14442-9_33","volume-title":"Multimedia Modeling International Conference","volume":"8936","author":"Sasaki Shoto","year":"2015","unstructured":"Shoto Sasaki, Tatsunori Hirai, Hayato Ohya, and Shigeo Morishima. 2015. Affective music recommendation system based on the mood of input video. In Multimedia Modeling International Conference, Vol. 8936. 299\u2013302."},{"key":"e_1_3_2_35_2","first-page":"1670","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In Proceedings of the International Conference on Machine Learning.1670\u20131679."},{"key":"e_1_3_2_36_2","first-page":"425","volume-title":"Proceedings of the IEEE\/ACM International Conference on Advances in Social Networking Analysis and Mining","author":"Shang Lanyu","year":"2020","unstructured":"Lanyu Shang, Zhang Daniel Yue, Khan Siamul Karim, Jialie Shen, and Dong Wang. 2020. CaMR: Towards connotation-aware music retrieval on social media with visual inputs. In Proceedings of the IEEE\/ACM International Conference on Advances in Social Networking Analysis and Mining. 425\u2013429."},{"key":"e_1_3_2_37_2","first-page":"5438","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Shen Dinghan","year":"2018","unstructured":"Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, and Lawrence Carin. 2018. Deconvolutional latent-variable model for text sequence matching. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32. 5438\u20135445."},{"key":"e_1_3_2_38_2","article-title":"Causal disentanglement for implicit recommendations with network information","author":"Sheth Paras","year":"2023","unstructured":"Paras Sheth, Ruocheng Guo, Lu Cheng, Huan Liu, and Kasim Sel\u00e7uk Candan. 2023. Causal disentanglement for implicit recommendations with network information. ACM Trans. Knowl. Discov. Data (2023).","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_2_39_2","first-page":"1","volume-title":"Proceedings of the ACM International Workshop on Crowdsourcing Multimedia","author":"Soleymani Mohammad","year":"2013","unstructured":"Mohammad Soleymani, Micheal N. Caro, Erik M. Schmidt, Cheng-Ya Sha, and Yi-Hsuan Yang. 2013. 1000 songs for emotional analysis of music. In Proceedings of the ACM International Workshop on Crowdsourcing Multimedia. 1\u20136."},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1145\/3240323.3240372","volume-title":"Proceedings of the ACM Conference on Recommender Systems.","author":"Steck Harald","year":"2018","unstructured":"Harald Steck. 2018. Calibrated recommendations. In Proceedings of the ACM Conference on Recommender Systems.154\u2013162."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.5555\/1200669"},{"key":"e_1_3_2_42_2","volume-title":"Proceedings of the European Conference on Computer Vision Workshops","author":"Sur\u00eds Didac","year":"2018","unstructured":"Didac Sur\u00eds, Amanda Duarte, Amaia Salvador, Jordi Torres, and Xavier Gir\u00f3-i Nieto. 2018. Cross-modal embeddings for video and audio retrieval. In Proceedings of the European Conference on Computer Vision Workshops."},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195068276.001.0001","volume-title":"The Biopsychology of Mood and Arousal","author":"Thayer Robert E.","year":"1990","unstructured":"Robert E. Thayer. 1990. The Biopsychology of Mood and Arousal. Oxford University Press."},{"key":"e_1_3_2_44_2","unstructured":"G. Tzanetakis. 1999. Gtzan Music\/Speech Collection. Retrieved from http:\/\/marsyas.info\/index.html"},{"key":"e_1_3_2_45_2","article-title":"A comprehensive survey on cross-modal retrieval","author":"Wang Kaiye","year":"2016","unstructured":"Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, and Liang Wang. 2016. A comprehensive survey on cross-modal retrieval. arXiv:1607.06215. Retrieved from https:\/\/arxiv.org\/abs\/1607.06215","journal-title":"arXiv:1607.06215"},{"key":"e_1_3_2_46_2","first-page":"1717","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wang Wenjie","year":"2021","unstructured":"Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021. Deconfounded recommendation for alleviating bias amplification. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1717\u20131725."},{"key":"e_1_3_2_47_2","first-page":"1288","volume-title":"Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Wang Wenjie","year":"2021","unstructured":"Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2021. Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1288\u20131297."},{"key":"e_1_3_2_48_2","first-page":"1162","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Wang Yujia","year":"2020","unstructured":"Yujia Wang, Wei Liang, Wanwan Li, Dingzeyu Li, and Lap-Fai Yu. 2020. Scene-aware background music synthesis. In Proceedings of the ACM International Conference on Multimedia. 1162\u20131170."},{"key":"e_1_3_2_49_2","first-page":"13005","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Wei Jiwei","year":"2020","unstructured":"Jiwei Wei, Xing Xu, Yang Yang, Yanli Ji, Zheng Wang, and Heng Tao Shen. 2020. Universal weighting metric learning for cross-modal matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.13005\u201313014."},{"key":"e_1_3_2_50_2","article-title":"Debiased visual question answering from feature and sample perspectives","volume":"34","author":"Wen Zhiquan","year":"2021","unstructured":"Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, and Qi Wu. 2021. Debiased visual question answering from feature and sample perspectives. Proceedings of the Advances in Neural Information Processing Systems Conference. 34 (2021).","journal-title":"Proceedings of the Advances in Neural Information Processing Systems Conference."},{"key":"e_1_3_2_51_2","first-page":"3870","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","volume":"19","author":"Wu Qiong","year":"2019","unstructured":"Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, and Lu Guan. 2019. PD-GAN: Adversarial learning for personalized diversity-promoting recommendation.. In Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 19. 3870\u20133876."},{"key":"e_1_3_2_52_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems Conference.","author":"Yang Chenxiao","year":"2022","unstructured":"Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, and Junchi Yan. 2022. Towards out-of-distribution sequential event prediction: A causal treatment. In Proceedings of the Advances in Neural Information Processing Systems Conference."},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1145\/3240323.3240355","volume-title":"Proceedings of the ACM Conference on Recommender Systems.","author":"Yang Longqi","year":"2018","unstructured":"Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge Belongie, and Deborah Estrin. 2018. Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In Proceedings of the ACM Conference on Recommender Systems.279\u2013287."},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3444944"},{"key":"e_1_3_2_55_2","unstructured":"Jing Yi Yaochen Zhu Jiayi Xie and Zhenzhong Chen. 2021. Cross-modal variational auto-encoder for content-based micro-video background music recommendation. IEEE Trans. Multimedia 25 (2021). 2898\u20132911."},{"key":"e_1_3_2_56_2","article-title":"Towards debiasing temporal sentence grounding in video","author":"Zhang Hao","year":"2021","unstructured":"Hao Zhang, Aixin Sun, Wei Jing, and Joey Tianyi Zhou. 2021. Towards debiasing temporal sentence grounding in video. arXiv:2111.04321.","journal-title":"arXiv:2111.04321"},{"key":"e_1_3_2_57_2","first-page":"11","volume-title":"Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhang Yang","year":"2021","unstructured":"Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 11\u201320."},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","unstructured":"Yihao Zhang Chu Zhao Weiwen Liao Wei Zhou and Meng Yuan. 2023. Asymmetrical attention networks fused autoencoder for debiased recommendation. ACM Trans. Intell. Syst. Technol. 14 6 (2023). 1\u201324.","DOI":"10.1145\/3596498"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","unstructured":"Boxiang Zhao Shuliang Wang Lianhua Chi Qi Li Xiaojia Liu and Jing Geng. 2023. Causal discovery via causal star graphs. ACM Trans. Knowl. Discov. Data 17 7 (2023). 1\u201324.","DOI":"10.1145\/3586997"},{"key":"e_1_3_2_60_2","first-page":"10394","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Zhen Liangli","year":"2019","unstructured":"Liangli Zhen, Peng Hu, Xu Wang, and Dezhong Peng. 2019. Deep supervised cross-modal retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.10394\u201310403."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650042","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:43Z","timestamp":1750291423000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,15]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3650042"],"URL":"https:\/\/doi.org\/10.1145\/3650042","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,15]]},"assertion":[{"value":"2023-05-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}