{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:16:49Z","timestamp":1784179009085,"version":"3.55.0"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CSC Scholarship offered by China Scholarship Council"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network\u2013based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.<\/jats:p>","DOI":"10.1145\/3473973","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T21:53:46Z","timestamp":1637186026000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Graph Neural Collaborative Topic Model for Citation Recommendation"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-7454","authenticated-orcid":false,"given":"Qianqian","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Manchester, Manchester, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jimin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Yun","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271696"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871517"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_5_2","first-page":"81","volume-title":"Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS\u201909)","author":"Chang Jonathan","year":"2009","unstructured":"Jonathan Chang and David M. Blei. 2009. Relational topic models for document networks. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS\u201909), David A. Van Dyk and Max Welling (Eds.). JMLR.org, 81\u201388. Retrieved from http:\/\/proceedings.mlr.press\/v5\/chang09a.html."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441762"},{"key":"e_1_3_2_7_2","first-page":"27","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920)","author":"Chen Lei","year":"2020","unstructured":"Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020. Revisiting graph-based collaborative filtering: A linear residual graph convolutional network approach. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920). AAAI Press, 27\u201334. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5330."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2361129"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383583.3398534"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772734"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.5555\/2898607.2898821"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00055"},{"key":"e_1_3_2_19_2","volume-title":"Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201914)","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR\u201914), Yoshua Bengio and Yann LeCun (Eds.). Retrieved from http:\/\/arxiv.org\/abs\/1312.6114."},{"key":"e_1_3_2_20_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. Retrieved from http:\/\/arxiv.org\/abs\/1611.07308."},{"key":"e_1_3_2_21_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917)","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). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=SJU4ayYgl."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2012.82"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/E14-1056"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504468"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806527"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098077"},{"key":"e_1_3_2_28_2","unstructured":"Ngo Van Linh Tran Xuan Bach and Khoat Than. 2020. Graph convolutional topic model for data streams. Retrieved from https:\/\/arxiv.org\/abs\/2003.06112."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403150"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458205"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313607"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.5555\/1642293.1642419"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/587078.587096"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045573"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401957"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109889"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623630"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685324"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685324"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.5555\/2981562.2981720"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273596"},{"key":"e_1_3_2_42_2","unstructured":"Wojciech Samek Thomas Wiegand and Klaus-Robert M\u00fcller. 2017. Explainable artificial intelligence: Understanding visualizing and interpreting deep learning models. Retrieved from http:\/\/arxiv.org\/abs\/1708.08296."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2833443"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806528"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742839"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917)","author":"Srivastava Akash","year":"2017","unstructured":"Akash Srivastava and Charles Sutton. 2017. Autoencoding variational inference for topic models. In Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=BybtVK9lg."},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277868"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01307-2_55"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/996350.996402"},{"key":"e_1_3_2_52_2","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. Retrieved from http:\/\/arxiv.org\/abs\/1706.02263."},{"key":"e_1_3_2_53_2","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2017. Graph attention networks. Retrieved from http:\/\/arxiv.org\/abs\/1710.10903."},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020480"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.5555\/2888116.2888141"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298626"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313411"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"e_1_3_2_65_2","first-page":"6267","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920)","author":"Wang Xiao","year":"2020","unstructured":"Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, and Qingyong Li. 2020. Multi-component graph convolutional collaborative filtering. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920). AAAI Press, 6267\u20136274. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6094."},{"key":"e_1_3_2_66_2","first-page":"3959","volume-title":"Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS\u201920)","author":"Wang Zhengjue","year":"2020","unstructured":"Zhengjue Wang, Chaojie Wang, Hao Zhang, Zhibin Duan, Mingyuan Zhou, and Bo Chen. 2020. Learning dynamic hierarchical topic graph with graph convolutional network for document classification. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS\u201920), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 3959\u20133969. Retrieved from http:\/\/proceedings.mlr.press\/v108\/wang20l.html."},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450045"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.333"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102614"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380102"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1065"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507230"},{"key":"e_1_3_2_74_2","first-page":"6737","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920)","author":"Zhang Ce","year":"2020","unstructured":"Ce Zhang and Hady W. Lauw. 2020. Topic modeling on document networks with adjacent-encoder. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920). AAAI Press, 6737\u20136745. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6152."},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098063"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240343"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045472"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367517"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1495"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3473973","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3473973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:47Z","timestamp":1750193327000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3473973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":79,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,7,31]]}},"alternative-id":["10.1145\/3473973"],"URL":"https:\/\/doi.org\/10.1145\/3473973","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]},"assertion":[{"value":"2020-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}