{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:35:19Z","timestamp":1778373319245,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819755745","type":"print"},{"value":"9789819755752","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5575-2_26","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T10:01:54Z","timestamp":1725184914000},"page":"352-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sparsity-Aware Personalized Pattern Extractor Network for\u00a0Music Multi-task Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-1889","authenticated-orcid":false,"given":"Shijia","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-5449","authenticated-orcid":false,"given":"Qiang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1621-049X","authenticated-orcid":false,"given":"Yilong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0238-110X","authenticated-orcid":false,"given":"Qimeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7022-8023","authenticated-orcid":false,"given":"Chuanjiang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Gao, C., et al.: Neural multi-task recommendation from multi-behavior data. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1554\u20131557. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00140"},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1930\u20131939. KDD \u201918, Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3219819.3220007","DOI":"10.1145\/3219819.3220007"},{"key":"26_CR3","doi-asserted-by":"publisher","unstructured":"Ma, X., et al.: Entire space multi-task model: an effective approach for estimating post-click conversion rate. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1137\u20131140. SIGIR \u201918, Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3209978.3210104","DOI":"10.1145\/3209978.3210104"},{"key":"26_CR4","doi-asserted-by":"publisher","unstructured":"O\u2019Brien, C., Liu, K.S., Neufeld, J., Barreto, R., Hunt, J.J.: An analysis of entire space multi-task models for post-click conversion prediction. In: Fifteenth ACM Conference on Recommender Systems, pp. 613\u2013619. RecSys \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3460231.3478852","DOI":"10.1145\/3460231.3478852"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Pan, X., et al.: MetaCVR: conversion rate prediction via meta learning in small-scale recommendation scenarios. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2110\u20132114 (2022)","DOI":"10.1145\/3477495.3531733"},{"key":"26_CR6","doi-asserted-by":"publisher","unstructured":"Qin, Z., Cheng, Y., Zhao, Z., Chen, Z., Metzler, D., Qin, J.: Multitask mixture of sequential experts for user activity streams. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3083\u20133091. KDD \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3394486.3403359","DOI":"10.1145\/3394486.3403359"},{"key":"26_CR7","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)"},{"key":"26_CR8","unstructured":"Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., Dean, J.: Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)"},{"key":"26_CR9","doi-asserted-by":"publisher","unstructured":"Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 269\u2013278. RecSys \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3383313.3412236","DOI":"10.1145\/3383313.3412236"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: ESCM2: entire space counterfactual multi-task model for post-click conversion rate estimation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363\u2013372 (2022)","DOI":"10.1145\/3477495.3531972"},{"key":"26_CR11","unstructured":"Wang, Q., Ji, Z., Liu, H., Zhao, B.: Deep Bayesian multi-target learning for recommender systems. CoRR abs\/1902.09154 (2019). http:\/\/arxiv.org\/abs\/1902.09154"},{"key":"26_CR12","doi-asserted-by":"publisher","unstructured":"Wen, H., et al.: Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2377\u20132386. SIGIR \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3397271.3401443","DOI":"10.1145\/3397271.3401443"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Wu, L., He, X., Wang, X., Zhang, K., Wang, M.: A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation. IEEE Trans. Knowl. Data Eng. 35(5), 4425\u20134445 (2022)","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"26_CR14","doi-asserted-by":"publisher","unstructured":"Xi, D., et al.: Modeling the sequential dependence among audience multi-step conversions with multi-task learning in targeted display advertising. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3745\u20133755. KDD \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3447548.3467071","DOI":"10.1145\/3447548.3467071"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, D., et al.: CTnoCVR: a novelty auxiliary task making the lower-CTR-higher-CVR upper. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2272\u20132276 (2022)","DOI":"10.1145\/3477495.3531843"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5575-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T10:05:43Z","timestamp":1725185143000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5575-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755745","9789819755752"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5575-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}