{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:11:18Z","timestamp":1784632278785,"version":"3.55.0"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"University Synergy Innovation Program of Anhui Province","award":["GXXT-2022-040"],"award-info":[{"award-number":["GXXT-2022-040"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61976077 and 62120106008"],"award-info":[{"award-number":["61976077 and 62120106008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"crossref","award":["2008085MF219 and 2108085MF212"],"award-info":[{"award-number":["2008085MF219 and 2108085MF212"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Provincial Natural Science Foundation of Anhui Higher Education Institution of China","award":["KJ2021A0040 and KJ2021A0043"],"award-info":[{"award-number":["KJ2021A0040 and KJ2021A0043"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is utilized to determine the optimal timing for requesting attribute information or suggesting items. However, existing methods consider user-preferred attributes independently and ignore that attributes may be of different importance to the same user, in the attribute and item selection phases, which limits the accuracy and interpretability of CRS. Inspired by this, we propose deep conversational path reasoning (DeepCPR), which involves constructing a reasoning path on a graph with a series of user-favored attributes. It utilizes the attention mechanism to thoroughly examine the connections between these attributes and provide improved explanations for which attributes to inquire about or which items to recommend. In DeepCPR, two deep-learning-based modules are proposed to realize attribute and item selection. In the first module, the sequence of attributes confirmed by the user in conversation is encoded with a gated graph neural network to obtain the user\u2019s long-term preference using a self-attention mechanism for the selection of candidate attributes. In the second module, a self-attention approach with more appropriate strategies is developed to dynamically select candidate items. In addition, to achieve fine-grained user preference modeling, a recurrent neural network is employed to aggregate the sequence of attributes that interact with the users. Numerous experimental evaluations conducted on four real CRS datasets show that the proposed method significantly outperforms existing advanced methods in terms of conversational recommendations.<\/jats:p>","DOI":"10.1145\/3610775","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T12:01:36Z","timestamp":1690286496000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["DeepCPR: Deep Path Reasoning Using Sequence of User-Preferred Attributes for Conversational Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5521-0764","authenticated-orcid":false,"given":"Huiting","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1161-0712","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-448X","authenticated-orcid":false,"given":"Peipei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2822-6321","authenticated-orcid":false,"given":"Cheng","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1594-7187","authenticated-orcid":false,"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Fellow, IEEE; Research Center for Knowledge Engineering at the Zhejiang Lab, China and Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186070"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1189"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403170"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159668"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219894"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939746"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570443"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2017913081"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462913"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00140"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2958808"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.06.002"},{"key":"e_1_3_1_14_2","first-page":"2766","volume-title":"Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6\u201312, 2007","author":"Gori Marco","year":"2007","unstructured":"Marco Gori and Augusto Pucci. 2007. ItemRank: A random-walk based scoring algorithm for recommender engines. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6\u201312, 2007, Manuela M. Veloso (Ed.). 2766\u20132771."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883069"},{"key":"e_1_3_1_16_2","first-page":"1024","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 1024\u20131034."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/358916.358995"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290958"},{"issue":"5","key":"e_1_3_1_21_2","first-page":"105:1\u2013105:36","article-title":"A survey on conversational recommender systems","volume":"54","author":"Jannach Dietmar","year":"2021","unstructured":"Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys 54, 5 (2021), 105:1\u2013105:36.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_22_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/245108.245126"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371769"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403258"},{"key":"e_1_3_1_26_2","first-page":"9748","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3\u20138, 2018, Montr\u00e9al, Canada","author":"Li Raymond","year":"2018","unstructured":"Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018. Towards deep conversational recommendations. In Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3\u20138, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 9748\u20139758."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446427"},{"key":"e_1_3_1_28_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2\u20134, 2016, Conference Track Proceedings","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated graph sequence neural networks. In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2\u20134, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411876"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"G. Linden S. Hanks and N. Lesh. 1999. Interactive assessment of user preference models: The automated travel assistant. In Proceedings of User Modeling\u201997 . 67\u201378.","DOI":"10.1007\/978-3-7091-2670-7_9"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Haifeng Liu Zheng Hu Ahmad Umair Mian Hui Tian and Xuzhen Zhu. 2014. A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56 (2014) 156\u2013166.","DOI":"10.1016\/j.knosys.2013.11.006"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.98"},{"key":"e_1_3_1_33_2","first-page":"627","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]","author":"Marlin Benjamin M.","year":"2003","unstructured":"Benjamin M. Marlin. 2003. Modeling user rating profiles for collaborative filtering. In Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada], Sebastian Thrun, Lawrence K. Saul, and Bernhard Sch\u00f6lkopf (Eds.). MIT Press, 627\u2013634."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45006-8_23"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412014"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117539"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532077"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.5555\/2857282"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2016.04.006"},{"key":"e_1_3_1_41_2","unstructured":"Roberto Saia Ludovico Boratto and Salvatore Mario Carta. 2016. A class-based strategy to user behavior modeling in recommender systems. Chapter 13 in Studies in Computational Intelligence 647 (2016) 1\u201320."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2008.2005605"},{"key":"e_1_3_1_44_2","article-title":"Explainable knowledge Graph-based recommendation via deep reinforcement learning","author":"Song Weiping","year":"2019","unstructured":"Weiping Song, Zhijian Duan, Ziqing Yang, Hao Zhu, Ming Zhang, and Jian Tang. 2019. Explainable knowledge Graph-based recommendation via deep reinforcement learning. CoRR abs\/1906.09506 (2019). arXiv:1906.09506.","journal-title":"CoRR"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/421425"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210002"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959114"},{"issue":"5","key":"e_1_3_1_48_2","first-page":"2137","article-title":"Neural attention frameworks for explainable recommendation","volume":"33","author":"Tal Omer","year":"2021","unstructured":"Omer Tal, Yang Liu, Jimmy X. Huang, Xiaohui Yu, and Bushra Aljbawi. 2021. Neural attention frameworks for explainable recommendation. IEEE Transactions on Knowledge and Data Engineering 33, 5 (2021), 2137\u20132150.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_49_2","first-page":"5998","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4\u20139, 2017, Long Beach, CA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998\u20136008."},{"key":"e_1_3_1_50_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_1_52_2","first-page":"346","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, The 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, January 27 - February 1, 2019","author":"Wu Shu","year":"2019","unstructured":"Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, The 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, January 27 - February 1, 2019. AAAI Press, 346\u2013353."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331203"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.463"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2327053"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380148"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271776"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512088"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.22"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.365"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401180"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3610775","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3610775","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:11Z","timestamp":1750178231000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3610775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,31]]}},"alternative-id":["10.1145\/3610775"],"URL":"https:\/\/doi.org\/10.1145\/3610775","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]},"assertion":[{"value":"2022-09-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}