{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T17:57:03Z","timestamp":1757699823244,"version":"3.41.0"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["92370119, 62032025, 62376113"],"award-info":[{"award-number":["92370119, 62032025, 62376113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key-Area Research and Development Program of Shandong Province","award":["2021CXGC010108"],"award-info":[{"award-number":["2021CXGC010108"]}]},{"name":"Jiangsu Science and Technology Program","award":["BE2020006-4"],"award-info":[{"award-number":["BE2020006-4"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            In the stock market, a successful investment requires a good balance between profits and risks. Based on the\n            <jats:italic>learning to rank<\/jats:italic>\n            paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial learning and propose a novel\n            <jats:italic>Split Variational Adversarial Training<\/jats:italic>\n            (SVAT) method for risk-aware stock recommendation. Essentially, SVAT encourages the stock model to be sensitive to adversarial perturbations of risky stock examples and enhances the model\u2019s risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on several real-world stock market datasets demonstrate the superiority of our SVAT method. By lowering the volatility of the stock-recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits. All the experimental data and source code are available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/drive.google.com\/drive\/folders\/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing\">https:\/\/drive.google.com\/drive\/folders\/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3643131","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T12:42:34Z","timestamp":1706186554000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial Training"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1755-6828","authenticated-orcid":false,"given":"Jiezhu","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-9639","authenticated-orcid":false,"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[{"name":"Data Science Research Center, Duke Kunshan University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Zhuhai, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/jofi.12364"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/UKSim.2014.67"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.6292\/AFPF.2012.05.04"},{"key":"e_1_3_2_5_2","article-title":"A survey on variational autoencoders from a GreenAI perspective","volume":"2103","author":"Asperti Andrea","year":"2021","unstructured":"Andrea Asperti, Davide Evangelista, and Elena Loli Piccolomini. 2021. A survey on variational autoencoders from a GreenAI perspective. CoRR abs\/2103.01071 (2021).","journal-title":"CoRR"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1086\/260062"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2020.113820"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2016.02.006"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5766"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/95"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.3233\/AIC-200629"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019495"},{"key":"e_1_3_2_14_2","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS\u201920)","author":"Dong Yinpeng","year":"2020","unstructured":"Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, and Hang Su. 2020. Adversarial distributional training for robust deep learning. In Annual Conference on Neural Information Processing Systems (NeurIPS\u201920), Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/5de8a36008b04a6167761fa19b61aa6c-Abstract.html"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20369"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/810"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3309547"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3451397"},{"key":"e_1_3_2_19_2","volume-title":"3rd International Conference on Learning Representations (ICLR\u201915)","author":"Goodfellow Ian J.","year":"2015","unstructured":"Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In 3rd International Conference on Learning Representations (ICLR\u201915), Yoshua Bengio and Yann LeCun (Eds.). Retrieved from http:\/\/arxiv.org\/abs\/1412.6572"},{"key":"e_1_3_2_20_2","volume-title":"Econometric Analysis.","author":"Greene William H.","year":"2003","unstructured":"William H. Greene. 2003. Econometric Analysis. (5th ed.). Prentice Hall."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1086\/499134"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.IMAGE.2021.116396"},{"key":"e_1_3_2_23_2","article-title":"Latent variable modelling using variational autoencoders: A survey","volume":"2206","author":"Kalingeri Vasanth","year":"2022","unstructured":"Vasanth Kalingeri. 2022. Latent variable modelling using variational autoencoders: A survey. CoRR abs\/2206.09891 (2022).","journal-title":"CoRR"},{"key":"e_1_3_2_24_2","volume-title":"3rd International Conference on Learning Representations (ICLR\u201915)","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations (ICLR\u201915), Yoshua Bengio and Yann LeCun (Eds.). Retrieved from http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_25_2","volume-title":"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 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_26_2","volume-title":"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 5th International Conference on Learning Representations (ICLR\u201917). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-021-00344-"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01110"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/2838731"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2021.2021143"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10489-022-03321-W"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1186\/S13638-022-02117-3"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i02.5587"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.84"},{"key":"e_1_3_2_35_2","volume-title":"6th International Conference on Learning Representations (ICLR\u201918)","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In 6th International Conference on Learning Representations (ICLR\u201918). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2021.01212106"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.2307\/2975974"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3036322"},{"key":"e_1_3_2_39_2","series-title":"JMLR Workshop and Conference Proceedings","first-page":"1727","volume-title":"33rd International Conference on Machine Learning (ICML\u201916)","volume":"48","author":"Miao Yishu","year":"2016","unstructured":"Yishu Miao, Lei Yu, and Phil Blunsom. 2016. Neural variational inference for text processing. In 33rd International Conference on Machine Learning (ICML\u201916) (JMLR Workshop and Conference Proceedings, Vol. 48), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1727\u20131736. Retrieved from http:\/\/proceedings.mlr.press\/v48\/miao16.html"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1080\/1351847042000199033"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jempfin.2004.02.003"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2022.108889"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"e_1_3_2_44_2","series-title":"JMLR Workshop and Conference Proceedings","first-page":"1521","volume-title":"33rd International Conference on Machine Learning (ICML\u201916)","volume":"48","author":"Rezende Danilo Jimenez","year":"2016","unstructured":"Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, and Daan Wierstra. 2016. One-shot generalization in deep generative models. In 33rd International Conference on Machine Learning (ICML\u201916) (JMLR Workshop and Conference Proceedings, Vol. 48), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1521\u20131529. Retrieved from http:\/\/proceedings.mlr.press\/v48\/rezende16.html"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2001.941023"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10212717"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16127"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10994-021-06112-5"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/1462198.1462204"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1964.tb02865.x"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-6622.2005.00042.x"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582560"},{"key":"e_1_3_2_53_2","volume-title":"2nd International Conference on Learning Representations (ICLR\u201914)","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations (ICLR\u201914), Yoshua Bengio and Yann LeCun (Eds.). Retrieved from http:\/\/arxiv.org\/abs\/1312.6199"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470644560"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/551"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3048309"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2019.00053"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/P18-1183"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2022.110211"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2022.108581"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.PROCS.2019.01.256"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00093"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.57"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643131","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3643131","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T16:31:21Z","timestamp":1750264281000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":62,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3643131"],"URL":"https:\/\/doi.org\/10.1145\/3643131","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"type":"print","value":"1046-8188"},{"type":"electronic","value":"1558-2868"}],"subject":[],"published":{"date-parts":[[2024,3,22]]},"assertion":[{"value":"2023-06-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}