{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:29:42Z","timestamp":1765499382640,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","funder":[{"name":"Australian Research Council Early Career Researcher Award","award":["DE230100366"],"award-info":[{"award-number":["DE230100366"]}]},{"name":"L\u2019Oreal-UNESCO For Women in Science 2023 Fellowship"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761257","type":"proceedings-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T23:59:18Z","timestamp":1762559958000},"page":"509-519","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ExplorAct: Context-Aware Next Action Recommendations for Interactive Data Exploration"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0833-6217","authenticated-orcid":false,"given":"Dinuka Manohara","family":"de Zoysa","sequence":"first","affiliation":[{"name":"The University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3769-3811","authenticated-orcid":false,"given":"James","family":"Bailey","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3503-4123","authenticated-orcid":false,"given":"Renata","family":"Borovica-Gajic","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Melbourne, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2011.5940430"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3546155.3546708"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609627"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357845"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389779"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14--1179"},{"key":"e_1_3_2_1_7_1","volume-title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv abs\/1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, \u00c7aglar G\u00fcl\u00e7ehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv abs\/1412.3555 (2014)."},{"key":"e_1_3_2_1_8_1","first-page":"2","article-title":"A Generalization of Bayesian Inference","volume":"30","author":"Dempster A. P.","year":"2018","unstructured":"A. P. Dempster. 2018. A Generalization of Bayesian Inference. Journal of the Royal Statistical Society: Series B (Methodological) 30, 2 (Dec. 2018), 205--232. doi:10. 1111\/j.2517--6161.1968.tb00722.x _eprint: https:\/\/academic.oup.com\/jrsssb\/articlepdf\/30\/2\/205\/49095334\/jrsssb_30_2_205.pdf.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"e_1_3_2_1_9_1","unstructured":"Lucas Deregnaucourt Alexis Lechervy Hind Laghmara and Samia Ainouz. 2023. An Evidential Deep Network Based on Dempster-Shafer Theory for Large Dataset. In Advances and Applications of DSmT for Information Fusion: Collected Works(Volume 5) Florentin Smarandache Jean Dezert and Albena Tchamova (Eds.). 907--914. https:\/\/normandie-univ.hal.science\/hal-04448387"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3565838.3565841"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2795218.2795226"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2599168"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-013-0311--4"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363913"},{"key":"e_1_3_2_1_15_1","volume-title":"White","author":"El-Mahassni Edwin D.","year":"2015","unstructured":"Edwin D. El-Mahassni and Karen L. White. 2015. A Discussion of Dempster-Shafer Theory and its Application to Identification Fusion. https:\/\/api.semanticscholar.org\/CorpusID:119579601"},{"key":"e_1_3_2_1_16_1","unstructured":"GitHub. [n. d.]. TAU-DB\/REACT-IDA-Recommendation-benchmark. https:\/\/github.com\/TAU-DB\/REACT-IDA-Recommendation-benchmark. [Accessed 23-05--2025]."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452762"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_1_19_1","unstructured":"Muhammad Hafeez. 2011. Application of Dempster Shafer Theory to Assess the Status of Sealed Fire in a Cole Mine. https:\/\/urn.kb.se\/resolve?urn=urn:nbn:se:bth-5323"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_21_1","volume-title":"Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH","author":"Weihua","year":"2020","unstructured":"Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-023-00816-x"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767843"},{"key":"e_1_3_2_1_24_1","unstructured":"Eugenie Y Lai Zainab Zolaktaf Mostafa Milani Omar AlOmeir Jianhao Cao and Rachel Pottinger. 2023. Workload-Aware Query Recommendation Using Deep Learning. (2023)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3555041.3589727"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457267"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219848"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3383126"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481967"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0--444--88738--2.50037--3"},{"key":"e_1_3_2_1_32_1","volume-title":"Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework. In Computer Vision -- ACCV","author":"Scheuermann Bj\u00f6rn","year":"2011","unstructured":"Bj\u00f6rn Scheuermann and Bodo Rosenhahn. 2011. Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework. In Computer Vision -- ACCV 2010, Ron Kimmel, Reinhard Klette, and Akihiro Sugimoto (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 426--439."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.15368\/theses.2009.104"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Kari Sentz and Scott Ferson. 2002. Combination of Evidence in Dempster-Shafer Theory. Technical Report SAND2002-0835. Sandia National Labs. Albuquerque NM (US); Sandia National Labs. Livermore CA (US). doi:10.2172\/800792","DOI":"10.2172\/800792"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2015.12.009"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","unstructured":"Amit Somech Tova Milo and Chai Ozeri. 2019. Predicting ''What is Interesting'' by Mining Interactive-Data-Analysis Session Logs. doi:10.5441\/002\/EDBT.2019.42","DOI":"10.5441\/002\/EDBT.2019.42"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSECP.2003.1193207"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData59044.2023.10386277"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023--15262--8"},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu,Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3--540--44792--4"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1177\/14759217211007130"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659458"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:24:52Z","timestamp":1765499092000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":43,"alternative-id":["10.1145\/3746252.3761257","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761257","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}