{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T04:10:56Z","timestamp":1765858256688,"version":"3.48.0"},"reference-count":99,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["U2441242"],"award-info":[{"award-number":["U2441242"]}],"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. Inf. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    <jats:bold>Conversational Recommender Systems (CRSs)<\/jats:bold>\n                    interact with users through natural language to provide recommendations and generate responses. Due to limited information in conversation, existing works utilize KGs or reviews to improve CRS. Despite achievements, they overlook co-occurrence relations which have shown effectiveness in collaborative filtering systems. In this work, we first propose a novel framework named\n                    <jats:bold>CoCRS<\/jats:bold>\n                    , aiming to incorporate\n                    <jats:bold>Co-occurrences into the Review-based Conversation Recommendation Systems<\/jats:bold>\n                    . In CoCRS, we mine co-occurrences from two aspects: (1)\n                    <jats:italic toggle=\"yes\">item and entity<\/jats:italic>\n                    , (2)\n                    <jats:italic toggle=\"yes\">user and item<\/jats:italic>\n                    . For the first one, we extract entities from redundant review texts by KG and construct a relation-aware item-entity heterogeneous graph. In the second aspect, we analyze review sentiments and construct a sentiment-aware user-item bipartite graph. We encode two graphs to obtain user and entity embeddings. Since users in CRS are anonymous, we generate a virtual similar user representation to match reviews with users. Besides, we capture time-aware preference representation from two-time dimensions. Finally, we generate word-level user representation with word-oriented KG and model user preference by integrating the above representations. Extensive experiments demonstrate that CoCRS outperforms baselines and the cold-start experiment highlights its robustness. The\n                    <jats:bold>Large Language Model (LLM)<\/jats:bold>\n                    experiment illustrates the significant role of co-occurrence relationships in LLM-based CRS. Our code are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Qin-lab-code\/CoCRS\">https:\/\/github.com\/Qin-lab-code\/CoCRS<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3771276","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T15:04:54Z","timestamp":1760627094000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Texts: Incorporating Co-occurrences into the Review-based Conversation Recommendation Systems"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5647-4989","authenticated-orcid":false,"given":"Haoyao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9270-1810","authenticated-orcid":false,"given":"Zhida","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8770-3838","authenticated-orcid":false,"given":"Xufeng","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6262-8579","authenticated-orcid":false,"given":"Jing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6807-9905","authenticated-orcid":false,"given":"Shuang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-2261","authenticated-orcid":false,"given":"Tianyu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-0384","authenticated-orcid":false,"given":"John C. S.","family":"Lui","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering Department, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. GPT-4 technical report. arXiv:2303.08774. Retrieved from https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"e_1_3_2_3_2","first-page":"722","volume-title":"Proceedings of the International Semantic Web Conference","author":"Auer S\u00f6ren","year":"2007","unstructured":"S\u00f6ren Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. DBpedia: A nucleus for a web of open data. In Proceedings of the International Semantic Web Conference. Springer, 722\u2013735."},{"key":"e_1_3_2_4_2","first-page":"46","volume-title":"Proceedings of the 15th International Conference on Machine Learning (ICML \u201998)","author":"Billsus Daniel","year":"1998","unstructured":"Daniel Billsus and Michael J. Pazzani. 1998. Learning collaborative information filters. In Proceedings of the 15th International Conference on Machine Learning (ICML \u201998), 46\u201354."},{"key":"e_1_3_2_5_2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, Vol. 33, 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_6_2","unstructured":"S\u00e9bastien Bubeck Varun Chandrasekaran Ronen Eldan Johannes Gehrke Eric Horvitz Ece Kamar Peter Lee Yin Tat Lee Yuanzhi Li Scott Lundberg et al. 2023. Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv:2303.12712. Retrieved from https:\/\/arxiv.org\/abs\/2303.12712"},{"key":"e_1_3_2_7_2","first-page":"1","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. In Proceedings of the 11th International Conference on Learning Representations, 1\u201315."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186070"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1189"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490477"},{"key":"e_1_3_2_12_2","unstructured":"Wei-Lin Chiang Zhuohan Li Ziqing Lin Ying Sheng Zhanghao Wu Hao Zhang Lianmin Zheng Siyuan Zhuang Yonghao Zhuang Joseph E. Gonzalez et al. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. Retrieved April 14 2023 from https:\/\/vicuna.lmsys.org."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939746"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570443"},{"key":"e_1_3_2_15_2","first-page":"2786","volume-title":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201924)","author":"Cui Justin","year":"2024","unstructured":"Justin Cui, Kai Dicarlantonio, Sara Kemper, Kathy Lin, Danjie Tang, Anton Korikov, and Scott Sanner. 2024. Retrieval-augmented conversational recommendation with prompt-based semi-structured natural language state tracking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201924), 2786\u20132790."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2506182.2506198"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3570640"},{"key":"e_1_3_2_18_2","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.), Association for Computational Linguistics, 4171\u20134186."},{"key":"e_1_3_2_19_2","unstructured":"Luke Friedman Sameer Ahuja David Allen Zhenning Tan Hakim Sidahmed Changbo Long Jun Xie Gabriel Schubiner Ajay Patel Harsh Lara et al. 2023. Leveraging large language models in conversational recommender systems. arXiv:2305.07961. Retrieved from https:\/\/arxiv.org\/abs\/2305.07961"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2795041"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591884"},{"key":"e_1_3_2_23_2","first-page":"1","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, Vol. 30, 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.654"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614949"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583536"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_30_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Kingma Diederik","year":"2015","unstructured":"Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR), 1\u201315."},{"key":"e_1_3_2_31_2","first-page":"1","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR \u201917)\u2013","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 \u201917), 1\u201314."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557072"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_35_2","first-page":"91","volume-title":"Recommender Systems Handbook","author":"Koren Yehuda","year":"2021","unstructured":"Yehuda Koren, Steffen Rendle, and Robert Bell. 2021. Advances in collaborative filtering. In Recommender Systems Handbook. Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.), Springer, 91\u2013142."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371769"},{"key":"e_1_3_2_37_2","first-page":"1","article-title":"Towards deep conversational recommendations","volume":"31","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 Advances in Neural Information Processing Systems, Vol. 31 (2018), 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532074"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.167"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583192"},{"key":"e_1_3_2_41_2","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)","author":"Lin Dekang","year":"2011","unstructured":"Dekang Lin, Yuji Matsumoto, and Rada Mihalcea (Eds.). 2011. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). Association for Computational Linguistics. Retrieved from https:\/\/aclanthology.org\/P11-2000.pdf."},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25567"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512104"},{"key":"e_1_3_2_44_2","unstructured":"Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan et al. 2024. DeepSeek-V3 technical report. arXiv:2412.19437. Retrieved from https:\/\/arxiv.org\/abs\/2412.19437"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544106"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591878"},{"key":"e_1_3_2_47_2","unstructured":"Zhuang Liu Yunpu Ma Yuanxin Ouyang and Zhang Xiong. 2021. Contrastive learning for recommender system. arXiv:2101.01317. Retrieved from https:\/\/arxiv.org\/abs\/2101.01317"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.99"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.139"},{"key":"e_1_3_2_50_2","first-page":"1","article-title":"Probabilistic matrix factorization","volume":"20","author":"Mnih Andriy","year":"2007","unstructured":"Andriy Mnih and Russ R. Salakhutdinov. 2007. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, Vol. 20, 1\u20138.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_51_2","first-page":"27","article-title":"Meta-CRS: A dynamic Meta-Learning approach for effective conversational recommender system","volume":"42","author":"Ni Yuxin","year":"2023","unstructured":"Yuxin Ni, Yunwen Xia, Hui Fang, Chong Long, Xinyu Kong, Daqian Li, Dong Yang, and Jie Zhang. 2023. Meta-CRS: A dynamic Meta-Learning approach for effective conversational recommender system. ACM Transactions on Information Systems 42, 1, Article 28 (Aug. 2023), 27 pages.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591876"},{"issue":"8","key":"e_1_3_2_53_2","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.9883"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2023.10.001"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3656639"},{"key":"e_1_3_2_60_2","first-page":"1","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"Shi Xingjian","year":"2015","unstructured":"Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-Chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems, Vol. 28, 1\u20139.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531927"},{"issue":"2","key":"e_1_3_2_62_2","first-page":"37","article-title":"Understanding and predicting user satisfaction with conversational recommender systems","volume":"42","author":"Siro Clemencia","year":"2023","unstructured":"Clemencia Siro, Mohammad Aliannejadi, and Maarten De Rijke. 2023. Understanding and predicting user satisfaction with conversational recommender systems. ACM Transactions on Information Systems 42, 2, Article 55 (Nov. 2023), 37 pages.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210002"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186154"},{"key":"e_1_3_2_66_2","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, Vol. 30, 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007958904918"},{"issue":"2","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3618106","article-title":"Multi-aspect graph contrastive learning for review-enhanced recommendation","volume":"42","author":"Wang Ke","year":"2023","unstructured":"Ke Wang, Yanmin Zhu, Tianzi Zang, Chunyang Wang, Kuan Liu, and Peibo Ma. 2023. Multi-aspect graph contrastive learning for review-enhanced recommendation. ACM Transactions on Information Systems 42, 2 (2023), 1\u201329.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.233"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.621"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298988"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532058"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688191"},{"key":"e_1_3_2_76_2","unstructured":"Canwen Xu Daya Guo Nan Duan and Julian McAuley. 2023. Baize: An open-source chat model with parameter-efficient tuning on self-chat data. arXiv:2304.01196. Retrieved from https:\/\/arxiv.org\/abs\/2304.01196"},{"key":"e_1_3_2_77_2","unstructured":"Jinfeng Xu Zheyu Chen Jinze Li Shuo Yang Wei Wang Xiping Hu and Edith C. H. Ngai. 2024. FourierKAN-GCF: Fourier Kolmogorov-Arnold network \u2013 An effective and efficient feature transformation for graph collaborative filtering. arXiv:2406.01034. Retrieved from https:\/\/arxiv.org\/abs\/2406.01034"},{"key":"e_1_3_2_78_2","first-page":"5453","volume-title":"Proceedings of the 35th International Conference on Machine LearningVol","volume":"80","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-Ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In Proceedings of the 35th International Conference on Machine Learning. Jennifer Dy and Andreas Krause (Eds.), Vol. 80, PMLR, 5453\u20135462."},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/3572834"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-naacl.4"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657924"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627159"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657893"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688133"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570426"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657748"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271776"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591737"},{"key":"e_1_3_2_90_2","unstructured":"Wayne Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang Zican Dong et al. 2023. A survey of large language models. arXiv:2303.18223. Retrieved from https:\/\/arxiv.org\/abs\/2303.18223"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018665"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.138"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.22"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403143"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.365"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1145\/3591469"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3701762"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498514"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714908"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103638"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3771276","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T04:09:15Z","timestamp":1765858155000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3771276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":99,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3771276"],"URL":"https:\/\/doi.org\/10.1145\/3771276","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"type":"print","value":"1046-8188"},{"type":"electronic","value":"1558-2868"}],"subject":[],"published":{"date-parts":[[2025,12,15]]},"assertion":[{"value":"2024-08-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}