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Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users\u2019 interests and naturally lead user-engaged dialogues with multiple conversational goals and diverse topics. Four tasks are often involved in MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and Response Generation. Most existing studies address only some of these tasks. To handle the whole problem of MG-CRS, modularized frameworks are adopted where each task is tackled independently without considering their interdependencies. In this work, we propose a novel Unified MultI-goal conversational recommeNDer system (UniMIND). Specifically, we unify these four tasks with different formulations into the same sequence-to-sequence paradigm. Prompt-based learning strategies are investigated to endow the unified model with the capability of multi-task learning. Finally, the overall learning and inference procedure consists of three stages, including multi-task learning, prompt-based tuning, and inference. Experimental results on two MG-CRS benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves state-of-the-art performance on all tasks with a unified model. Extensive analyses and discussions are provided for shedding some new perspectives for MG-CRS.<\/jats:p>","DOI":"10.1145\/3570640","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T12:35:17Z","timestamp":1667565317000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-5943","authenticated-orcid":false,"given":"Yang","family":"Deng","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1492-4952","authenticated-orcid":false,"given":"Wenxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6090-2984","authenticated-orcid":false,"given":"Weiwen","family":"Xu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6540-0601","authenticated-orcid":false,"given":"Wenqiang","family":"Lei","sequence":"additional","affiliation":[{"name":"Sichuan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-7807","authenticated-orcid":false,"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[{"name":"Sea-NExT Joint Lab, National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5479-377X","authenticated-orcid":false,"given":"Wai","family":"Lam","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"12535","volume-title":"Proceedings of AAAI 2021","author":"Bai Jiaqi","year":"2021","unstructured":"Jiaqi Bai, Ze Yang, Xinnian Liang, Wei Wang, and Zhoujun Li. 2021. 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