{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:58:22Z","timestamp":1764784702951,"version":"3.41.0"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Key D&R Program of China","award":["2019YFB1600704"],"award-info":[{"award-number":["2019YFB1600704"]}]},{"DOI":"10.13039\/501100006469","name":"FDCT","doi-asserted-by":"crossref","award":["FDCT\/0045\/2019\/A1, FDCT\/0007\/2018\/A1"],"award-info":[{"award-number":["FDCT\/0045\/2019\/A1, FDCT\/0007\/2018\/A1"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"crossref"}]},{"name":"GSTIC","award":["EF005\/FST-GZG\/2019\/GSTIC"],"award-info":[{"award-number":["EF005\/FST-GZG\/2019\/GSTIC"]}]},{"DOI":"10.13039\/501100004733","name":"University of Macau","doi-asserted-by":"crossref","award":["MYRG2018-00129-FST"],"award-info":[{"award-number":["MYRG2018-00129-FST"]}],"id":[{"id":"10.13039\/501100004733","id-type":"DOI","asserted-by":"crossref"}]},{"name":"GDST","award":["2019B111106001"],"award-info":[{"award-number":["2019B111106001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the\n            <jats:italic>multi-topic-thread evolution<\/jats:italic>\n            . Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward\u2013forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.\n          <\/jats:p>","DOI":"10.1145\/3451530","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T21:06:18Z","timestamp":1626815178000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Recurrent Coupled Topic Modeling over Sequential Documents"],"prefix":"10.1145","volume":"16","author":[{"given":"Jinjin","family":"Guo","sequence":"first","affiliation":[{"name":"University of Macau Macau S.A.R 999078"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longbing","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Technology Sydney Ultimo, New South Wales 2007 Sydney"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiguo","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Macau Macau S.A.R 999078"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Ayan Acharya Joydeep Ghosh and Mingyuan Zhou. 2015. Nonparametric bayesian factor analysis for dynamic count matrices. In AISTATS. 1\u20139.  Ayan Acharya Joydeep Ghosh and Mingyuan Zhou. 2015. Nonparametric bayesian factor analysis for dynamic count matrices. In AISTATS. 1\u20139."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219995"},{"key":"e_1_2_1_3_1","volume-title":"Jacob Eisenstein, Alex Smola, and Eric Xing.","author":"Ahmed Amr","year":"2011","unstructured":"Amr Ahmed , Qirong Ho , Choon Hui Teo , Jacob Eisenstein, Alex Smola, and Eric Xing. 2011 . Online inference for the infinite topic-cluster model: Storylines from streaming text. In AISTATS. 101\u2013109. Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alex Smola, and Eric Xing. 2011. Online inference for the infinite topic-cluster model: Storylines from streaming text. In AISTATS. 101\u2013109."},{"volume-title":"Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering","author":"Ahmed Amr","key":"e_1_2_1_4_1","unstructured":"Amr Ahmed and Eric Xing . 2008. Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering . In SDM. SIAM , 219\u2013230. Amr Ahmed and Eric Xing. 2008. Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering. In SDM. SIAM, 219\u2013230."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3023549.3023552"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939781"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883046"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/2976248.2976267"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143859"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2014.08.007"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Xilun Chen K. Sel\u00e7uk Candan and Maria Luisa Sapino. 2018. IMS-DTM: Incremental multi-scale dynamic topic models. In AAAI.  Xilun Chen K. Sel\u00e7uk Candan and Maria Luisa Sapino. 2018. IMS-DTM: Incremental multi-scale dynamic topic models. In AAAI.","DOI":"10.1609\/aaai.v32i1.11988"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Xin Cheng Duoqian Miao Can Wang and Longbing Cao. 2013. Coupled term-term relation analysis for document clustering. In IJCNN. 1\u20138.  Xin Cheng Duoqian Miao Can Wang and Longbing Cao. 2013. Coupled term-term relation analysis for document clustering. In IJCNN. 1\u20138.","DOI":"10.1109\/IJCNN.2013.6706853"},{"key":"e_1_2_1_14_1","unstructured":"Rajarshi Das Manzil Zaheer and Chris Dyer. 2015. Gaussian LDA for topic models with word embeddings. In ACL. 795\u2013804.  Rajarshi Das Manzil Zaheer and Chris Dyer. 2015. Gaussian LDA for topic models with word embeddings. In ACL. 795\u2013804."},{"key":"e_1_2_1_15_1","unstructured":"Adji B. Dieng Wang Chong Jianfeng Gao and John Paisley. 2016. TopicRNN: A recurrent neural network with long-range semantic dependency. In ICLR.  Adji B. Dieng Wang Chong Jianfeng Gao and John Paisley. 2016. TopicRNN: A recurrent neural network with long-range semantic dependency. In ICLR."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327484"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886679"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783411"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2021039"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"e_1_2_1_21_1","unstructured":"Dandan Guo Bo Chen Ruiying Lu and Mingyuan Zhou. 2020. Recurrent hierarchical topic-guided RNN for language generation. In ICML.  Dandan Guo Bo Chen Ruiying Lu and Mingyuan Zhou. 2020. Recurrent hierarchical topic-guided RNN for language generation. In ICML."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327757.3327936"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172128"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016505"},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Pankaj Gupta Subburam Rajaram Hinrich Sch\u00fctze and Bernt Andrassy. 2018. Deep temporal-recurrent-replicated-softmax for topical trends over time. NAACL-HLT.  Pankaj Gupta Subburam Rajaram Hinrich Sch\u00fctze and Bernt Andrassy. 2018. Deep temporal-recurrent-replicated-softmax for topical trends over time. NAACL-HLT.","DOI":"10.18653\/v1\/N18-1098"},{"key":"e_1_2_1_26_1","unstructured":"Ben Hamner. [n.d.]. NIPS Papers.  Ben Hamner. [n.d.]. NIPS Papers."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.12.007"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098074"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2984093.2984273"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369873"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/1661445.1661674"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835889"},{"key":"e_1_2_1_33_1","first-page":"1427","article-title":"Scalable generalized dynamic topic models","volume":"84","author":"J\u00e4hnichen Patrick","year":"2018","unstructured":"Patrick J\u00e4hnichen , Florian Wenzel , Marius Kloft , and Stephan Mandt . 2018 . Scalable generalized dynamic topic models . In AISTATS , Vol. 84. 1427 \u2013 1435 . Patrick J\u00e4hnichen, Florian Wenzel, Marius Kloft, and Stephan Mandt. 2018. Scalable generalized dynamic topic models. In AISTATS, Vol. 84. 1427\u20131435.","journal-title":"AISTATS"},{"key":"e_1_2_1_34_1","series-title":"Series D","volume-title":"A new approach to linear filtering and prediction problems. J. Basic Eng. 82","author":"Kalman Rudolph Emil","year":"1960","unstructured":"Rudolph Emil Kalman . 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82 , Series D ( 1960 ), 35\u201345. Rudolph Emil Kalman. 1960. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, Series D (1960), 35\u201345."},{"key":"e_1_2_1_35_1","unstructured":"Jey Han Lau Timothy Baldwin and Trevor Cohn. 2017. Topically driven neural language model. In ACL. 355\u2013365.  Jey Han Lau Timothy Baldwin and Trevor Cohn. 2017. Topically driven neural language model. In ACL. 355\u2013365."},{"key":"e_1_2_1_36_1","unstructured":"Jey Han Lau David Newman and Timothy Baldwin. 2014. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In ACL. 530\u2013539.  Jey Han Lau David Newman and Timothy Baldwin. 2014. Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. In ACL. 530\u2013539."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3091108"},{"key":"e_1_2_1_38_1","unstructured":"Yaqiong Li Xuhui Fan Ling Chen Bin Li and Scott A Sisson. 2020. Recurrent Dirichlet belief networks for interpretable dynamic relational data modelling. In IJCAI.  Yaqiong Li Xuhui Fan Ling Chen Bin Li and Scott A Sisson. 2020. Recurrent Dirichlet belief networks for interpretable dynamic relational data modelling. In IJCAI."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014269"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939748"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969442.2969625"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999959"},{"key":"#cr-split#-e_1_2_1_43_1.1","unstructured":"Rishabh Misra. 2018. News Category Dataset. DOI:DOI:https:\/\/doi.org\/10.13140\/RG.2.2.20331.18729 10.13140\/RG.2.2.20331.18729"},{"key":"#cr-split#-e_1_2_1_43_1.2","unstructured":"Rishabh Misra. 2018. News Category Dataset. DOI:DOI:https:\/\/doi.org\/10.13140\/RG.2.2.20331.18729"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281249"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685324"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454358"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157657"},{"key":"e_1_2_1_48_1","volume-title":"Sutton","author":"Srivastava Akash","year":"2017","unstructured":"Akash Srivastava and Charles A . Sutton . 2017 . Autoencoding variational inference for topic models. In ICLR. Akash Srivastava and Charles A. Sutton. 2017. Autoencoding variational inference for topic models. In ICLR."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999611.2999671"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000302"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/3023476.3023545"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/2540128.2540377"},{"key":"e_1_2_1_54_1","unstructured":"Wenlin Wang Zhe Gan Wenqi Wang Dinghan Shen Jiaji Huang Wei Ping Sanjeev Satheesh and Lawrence Carin. 2018. Topic compositional neural language model. In AISTATS. 356\u2013365.  Wenlin Wang Zhe Gan Wenqi Wang Dinghan Shen Jiaji Huang Wei Ping Sanjeev Satheesh and Lawrence Carin. 2018. Topic compositional neural language model. In AISTATS. 356\u2013365."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339552"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104468"},{"volume-title":"Topic discovery for streaming short texts with CTM","author":"Xu Yunfeng","key":"e_1_2_1_57_1","unstructured":"Yunfeng Xu , Hua Xu , Longxia Zhu , Hanyong Hao , Junhui Deng , Xiaomin Sun , and Xiaoli Bai . 2018. Topic discovery for streaming short texts with CTM . In IJCNN. IEEE , 1\u20137. Yunfeng Xu, Hua Xu, Longxia Zhu, Hanyong Hao, Junhui Deng, Xiaomin Sun, and Xiaoli Bai. 2018. Topic discovery for streaming short texts with CTM. In IJCNN. IEEE, 1\u20137."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488514"},{"key":"e_1_2_1_59_1","unstructured":"Sikun Yang and Heinz Koeppl. 2018. Dependent relational gamma process models for longitudinal networks. In ICML. 5551\u20135560.  Sikun Yang and Heinz Koeppl. 2018. Dependent relational gamma process models for longitudinal networks. In ICML. 5551\u20135560."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220094"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939841"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098027"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911519"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327757.3327892"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.211"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451530","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3451530","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:59Z","timestamp":1750197779000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,20]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2,28]]}},"alternative-id":["10.1145\/3451530"],"URL":"https:\/\/doi.org\/10.1145\/3451530","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2021,7,20]]},"assertion":[{"value":"2020-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}