{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:04:47Z","timestamp":1742943887218,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031208904"},{"type":"electronic","value":"9783031208911"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20891-1_32","type":"book-chapter","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:03:02Z","timestamp":1667779382000},"page":"443-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Click is Not Equal to\u00a0Purchase: Multi-task Reinforcement Learning for\u00a0Multi-behavior Recommendation"],"prefix":"10.1007","author":[{"given":"Huiwang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Xian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjing","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"32_CR1","first-page":"10734","volume":"32","author":"X Bai","year":"2019","unstructured":"Bai, X., Guan, J., Wang, H.: A model-based reinforcement learning with adversarial training for online recommendation. Adv. Neural. Inf. Process. Syst. 32, 10734\u201310745 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1\u20132","key":"32_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1022140919877","volume":"13","author":"AG Barto","year":"2003","unstructured":"Barto, A.G., Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Discret. Event Dyn. Syst. 13(1\u20132), 41\u201377 (2003)","journal-title":"Discret. Event Dyn. Syst."},{"key":"32_CR3","unstructured":"Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. arXiv preprint arXiv:1301.7363 (2013)"},{"issue":"1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41\u201375 (1997). https:\/\/doi.org\/10.1023\/A:1007379606734","journal-title":"Mach. Learn."},{"issue":"1","key":"32_CR5","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79\u201387 (1991)","journal-title":"Neural Comput."},{"issue":"4","key":"32_CR6","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst."},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Exploiting aesthetic preference in deep cross networks for cross-domain recommendation. In: Proceedings of the Web Conference, pp. 2768\u20132774 (2020)","DOI":"10.1145\/3366423.3380036"},{"key":"32_CR8","doi-asserted-by":"crossref","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 (2018)","DOI":"10.1145\/3219819.3220007"},{"key":"32_CR9","unstructured":"Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195\u2013204 (2000)","DOI":"10.1145\/336597.336662"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Pei, C., et al.: Value-aware recommendation based on reinforcement profit maximization. In: The World Wide Web Conference, pp. 3123\u20133129 (2019)","DOI":"10.1145\/3308558.3313404"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Pinto, L., Gupta, A.: Learning to push by grasping: using multiple tasks for effective learning. In: IEEE International Conference on Robotics and Automation, pp. 2161\u20132168 (2017)","DOI":"10.1109\/ICRA.2017.7989249"},{"key":"32_CR13","first-page":"1265","volume":"6","author":"G Shani","year":"2005","unstructured":"Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265\u20131295 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/BF00992700","volume":"8","author":"SP Singh","year":"1992","unstructured":"Singh, S.P.: Transfer of learning by composing solutions of elemental sequential tasks. Mach. Learn. 8, 323\u2013339 (1992)","journal-title":"Mach. Learn."},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: Fourteenth ACM Conference on Recommender Systems, pp. 269\u2013278 (2020)","DOI":"10.1145\/3383313.3412236"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Wang, P., Fan, Y., Xia, L., Zhao, W.X., Niu, S., Huang, J.: KERL: a knowledge-guided reinforcement learning model for sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 209\u2013218 (2020)","DOI":"10.1145\/3397271.3401134"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Wilson, A., Fern, A., Ray, S., Tadepalli, P.: Multi-task reinforcement learning: a hierarchical Bayesian approach. In: Proceedings of the 24th International Conference on Machine Learning, vol. 227, pp. 1015\u20131022 (2007)","DOI":"10.1145\/1273496.1273624"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Xiao, K., Ye, Z., Zhang, L., Zhou, W., Ge, Y., Deng, Y.: Multi-user mobile sequential recommendation for route optimization. ACM Trans. Knowl. Discov. Data 14(5), 52:1\u201352:28 (2020)","DOI":"10.1145\/3360048"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Xie, R., Zhang, S., Wang, R., Xia, F., Lin, L.: Hierarchical reinforcement learning for integrated recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4521\u20134528 (2021)","DOI":"10.1609\/aaai.v35i5.16580"},{"key":"32_CR20","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.neucom.2020.10.066","volume":"423","author":"C Xu","year":"2021","unstructured":"Xu, C., et al.: Long- and short-term self-attention network for sequential recommendation. Neurocomputing 423, 580\u2013589 (2021)","journal-title":"Neurocomputing"},{"key":"32_CR21","first-page":"4767","volume":"33","author":"R Yang","year":"2020","unstructured":"Yang, R., Xu, H., Wu, Y., Wang, X.: Multi-task reinforcement learning with soft modularization. Adv. Neural. Inf. Process. Syst. 33, 4767\u20134777 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, D., Zhang, L., Zhang, B., Zheng, L., Bao, Y., Yan, W.: MaHRL: multi-goals abstraction based deep hierarchical reinforcement learning for recommendations. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871\u2013880 (2020)","DOI":"10.1145\/3397271.3401170"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, P., Zhao, L., Liu, Y., Sheng, V.S., Zhou, X.: Variational self-attention network for sequential recommendation. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1559\u20131570 (2021)","DOI":"10.1109\/ICDE51399.2021.00138"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, L., Ding, Z., Xia, L., Tang, J., Yin, D.: Recommendations with negative feedback via pairwise deep reinforcement learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1040\u20131048 (2018)","DOI":"10.1145\/3219819.3219886"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Zheng, G., et al.: DRN: a deep reinforcement learning framework for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 167\u2013176 (2018)","DOI":"10.1145\/3178876.3185994"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20891-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:43:45Z","timestamp":1667781825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20891-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208904","9783031208911"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20891-1_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Biarritz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2022.sigappfr.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The proceedings include 3 demo papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}