{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:21:11Z","timestamp":1784542871112,"version":"3.55.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s10479-022-04834-w","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T19:02:36Z","timestamp":1656702156000},"page":"329-348","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Identifying purchase intention through deep learning: analyzing the Q &amp;D text of an E-Commerce platform"],"prefix":"10.1007","volume":"339","author":[{"given":"Jing","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9423-5366","authenticated-orcid":false,"given":"Xufeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"4834_CR1","doi-asserted-by":"crossref","unstructured":"An, C., Huang, J., Chang, S., & Huang, Z. (2016). Question similarity modeling with bidirectional long short-term memory neural network. In 2016 IEEE first international conference on data science in cyberspace (DSC) (pp. 318\u2013322). IEEE.","DOI":"10.1109\/DSC.2016.13"},{"key":"4834_CR2","unstructured":"Balakrishnan, J., & Dwivedi, Y.K. (2021). Conversational commerce: entering the next stage of ai-powered digital assistants. Annals of Operations Research, pp. 1\u201335."},{"issue":"2","key":"4834_CR3","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10479-018-3067-9","volume":"284","author":"S Basso","year":"2020","unstructured":"Basso, S., Ceselli, A., & Tettamanzi, A. (2020). Random sampling and machine learning to understand good decompositions. Annals of Operations Research, 284(2), 501\u2013526.","journal-title":"Annals of Operations Research"},{"key":"4834_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Frasconi, P., & Simard, P. (1993). The problem of learning long-term dependencies in recurrent networks. In IEEE international conference on neural networks (pp. 1183\u20131188). IEEE.","DOI":"10.1109\/ICNN.1993.298725"},{"key":"4834_CR5","unstructured":"Choi, J.I., Kallumadi, S., Mitra, B., Agichtein, E., & Javed, F. (2020). Semantic product search for matching structured product catalogs in e-commerce. arXiv preprint arXiv:2008.08180."},{"key":"4834_CR6","unstructured":"Chollet, F.: Keras. https:\/\/github.com\/fchollet\/keras."},{"key":"4834_CR7","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., & LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), (vol. 1, pp. 539\u2013546). IEEE.","DOI":"10.1109\/CVPR.2005.202"},{"key":"4834_CR8","doi-asserted-by":"crossref","unstructured":"Das, A., Yenala, H., Chinnakotla, M., & Shrivastava, M. (2016). Together we stand: Siamese networks for similar question retrieval. In proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers, pp. 378\u2013387).","DOI":"10.18653\/v1\/P16-1036"},{"key":"4834_CR9","unstructured":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"4834_CR10","doi-asserted-by":"publisher","first-page":"120903","DOI":"10.1016\/j.techfore.2021.120903","volume":"170","author":"P Eachempati","year":"2021","unstructured":"Eachempati, P., Srivastava, P. R., Kumar, A., Tan, K. H., & Gupta, S. (2021). Validating the impact of accounting disclosures on stock market: A deep neural network approach. Technological Forecasting and Social Change, 170, 120903.","journal-title":"Technological Forecasting and Social Change"},{"key":"4834_CR11","first-page":"2843","volume":"27","author":"B Fu","year":"2016","unstructured":"Fu, B., & Liu, T. (2016). Implicit user consumption intent recognition in social media. Journal of Software, 27, 2843\u20132854.","journal-title":"Journal of Software"},{"issue":"8","key":"4834_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735\u20131780.","journal-title":"Neural computation"},{"key":"4834_CR13","doi-asserted-by":"crossref","unstructured":"Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. (2013). Learning deep structured semantic models for web search using clickthrough data. In proceedings of the 22nd ACM international conference on information & knowledge management,(pp. 2333\u20132338).","DOI":"10.1145\/2505515.2505665"},{"key":"4834_CR14","unstructured":"Jia, Y., Han, D., Lin, H., Wang, G., & Xia, l. (2020). Consumption intent recognition algorithms for weibo users. Acta Scientiarum Naturalium Universitatis Pekinensis,56, (pp. 68\u201374)."},{"key":"4834_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, A., Singh, J.P., Dwivedi, Y.K., & Rana, N.P. (2020). A deep multi-modal neural network for informative twitter content classification during emergencies. Annals of Operations Research, (pp. 1\u201332).","DOI":"10.1007\/s10479-020-03514-x"},{"key":"4834_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2021.113728","volume":"155","author":"A Kumar","year":"2022","unstructured":"Kumar, A., Gopal, R. D., Shankar, R., & Tan, K. H. (2022). Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering. Decision Support Systems, 155, 113728.","journal-title":"Decision Support Systems"},{"key":"4834_CR17","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.indmarman.2019.05.003","volume":"90","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Shankar, R., & Aljohani, N. R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial marketing management, 90, 493\u2013507.","journal-title":"Industrial marketing management"},{"key":"4834_CR18","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.jocs.2017.06.006","volume":"27","author":"A Kumar","year":"2018","unstructured":"Kumar, A., Shankar, R., & Thakur, L. S. (2018). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of computational science, 27, 428\u2013439.","journal-title":"Journal of computational science"},{"issue":"2","key":"4834_CR19","first-page":"1","volume":"15","author":"CL Kwek","year":"1970","unstructured":"Kwek, C. L., Tan, H. P., & Lau, T.-C. (1970). Investigating the shopping orientations on online purchase intention in the e-commerce environment: a malaysian study. The Journal of Internet Banking and Commerce, 15(2), 1\u201321.","journal-title":"The Journal of Internet Banking and Commerce"},{"key":"4834_CR20","doi-asserted-by":"crossref","unstructured":"Lee, D.-G., Lee, K.-H., & Lee, S.-Y. (2015). Implicit shopping intention recognition with eye tracking data and response time. In proceedings of the 3rd international conference on human-agent interaction, (pp. 295\u2013298).","DOI":"10.1145\/2814940.2815001"},{"key":"4834_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., Du, Y., & Wang, S. (2017). Mining implicit intention using attention-based rnn encoder-decoder model. In International Conference on Intelligent Computing, (pp. 413\u2013424). Springer.","DOI":"10.1007\/978-3-319-63315-2_36"},{"key":"4834_CR22","doi-asserted-by":"crossref","unstructured":"Liao, Y., Peng, Y., Shi, S., Shi, V., & Yu, X. (2020). Early box office prediction in china\u2019s film market based on a sLiaotacking fusion model. Annals of Operations Research, (pp. 1\u201318).","DOI":"10.1007\/s10479-020-03804-4"},{"key":"4834_CR23","unstructured":"Luo, X., Gong, Y., & Chen, X. (2018). Central intention identification for natural language search query in e-commerce. In eCOM@ SIGIR."},{"key":"4834_CR24","doi-asserted-by":"crossref","unstructured":"Mai, L., & Le, B. (2020). Joint sentence and aspect-level sentiment analysis of product comments.Annals of Operations research, (pp. 1\u201321).","DOI":"10.1007\/s10479-020-03534-7"},{"key":"4834_CR25","unstructured":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781."},{"key":"4834_CR26","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., Cernock\u1ef3, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Interspeech, (vol. 2, pp. 1045\u20131048). Makuhari.","DOI":"10.21437\/Interspeech.2010-343"},{"key":"4834_CR27","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In advances in neural information processing systems, (pp. 3111\u20133119)."},{"key":"4834_CR28","doi-asserted-by":"crossref","unstructured":"Nassif, H., Mohtarami, M., & Glass, J. (2016). Learning semantic relatedness in community question answering using neural models. In proceedings of the 1st workshop on representation learning for NLP, (pp. 137\u2013147).","DOI":"10.18653\/v1\/W16-1616"},{"key":"4834_CR29","doi-asserted-by":"crossref","unstructured":"Nikhil, N., & Srivastava, M.M. (2017). Content based document recommender using deep learning. In 2017 International Conference on Inventive Computing and Informatics (ICICI), (pp. 486\u2013489). IEEE.","DOI":"10.1109\/ICICI.2017.8365399"},{"key":"4834_CR30","unstructured":"Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., & Ward, R. (2014). Semantic modelling with long-short-term memory for information retrieval. arXiv preprint arXiv:1412.6629."},{"issue":"4","key":"4834_CR31","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TASLP.2016.2520371","volume":"24","author":"H Palangi","year":"2016","unstructured":"Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., et al. (2016). Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE\/ACM Transactions on Audio, Speech, and Language Processing, 24(4), 694\u2013707.","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"4834_CR32","unstructured":"Peng, X. (2018). Research on recognition method in customer purchase intention based on intelligent customer service learning. Master\u2019s thesis, Shandong University of Finance and Economics."},{"key":"4834_CR33","doi-asserted-by":"crossref","unstructured":"Qayyum, A., Razzak, I., Tanveer, M., & Kumar, A. (2021). Depth-wise dense neural network for automatic covid19 infection detection and diagnosis. Annals of operations research, (pp. 1\u201321).","DOI":"10.1007\/s10479-021-04154-5"},{"key":"4834_CR34","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1360\/N112016-00306","volume":"47","author":"Y Qian","year":"2017","unstructured":"Qian, Y., Ding, X., Liu, T., & Yiheng, C. (2017). Identification method of user\u2019s travel consumption intention in chatting robot. Sci Sin Inform, 47, 997\u20131007.","journal-title":"Sci Sin Inform"},{"key":"4834_CR35","unstructured":"Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training."},{"key":"4834_CR36","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.imavis.2018.04.004","volume":"75","author":"P Rodr\u00edguez","year":"2018","unstructured":"Rodr\u00edguez, P., Bautista, M. A., Gonzalez, J., & Escalera, S. (2018). Beyond one-hot encoding: Lower dimensional target embedding. Image and Vision Computing, 75, 21\u201331.","journal-title":"Image and Vision Computing"},{"key":"4834_CR37","unstructured":"Rong, X. (2014). word2vec parameter learning explained. arXiv preprint arXiv:1411.2738."},{"key":"4834_CR38","unstructured":"Seger, C. (2018). An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing."},{"key":"4834_CR39","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.jbusres.2021.08.035","volume":"137","author":"P Sengupta","year":"2021","unstructured":"Sengupta, P., Biswas, B., Kumar, A., Shankar, R., & Gupta, S. (2021). Examining the predictors of successful airbnb bookings with hurdle models: Evidence from europe, australia, usa and asia-pacific cities. Journal of Business Research, 137, 538\u2013554.","journal-title":"Journal of Business Research"},{"key":"4834_CR40","doi-asserted-by":"crossref","unstructured":"Shen, Y., He, X., Gao, J., Deng, L., & Mesnil, G. (2014). A latent semantic model with convolutional-pooling structure for information retrieval. In proceedings of the 23rd ACM international conference on conference on information and knowledge management, (pp. 101\u2013110).","DOI":"10.1145\/2661829.2661935"},{"key":"4834_CR41","doi-asserted-by":"crossref","unstructured":"Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Grue\u00a0Simonsen, J., & Nie, J.-Y. (2015). A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In proceedings of the 24th ACM international on conference on information and knowledge management, (pp. 553\u2013562).","DOI":"10.1145\/2806416.2806493"},{"issue":"1","key":"4834_CR42","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s10479-020-03734-1","volume":"303","author":"X Sun","year":"2021","unstructured":"Sun, X., Xu, W., Jiang, H., & Wang, Q. (2021). A deep multitask learning approach for air quality prediction. Annals of Operations Research, 303(1), 51\u201379.","journal-title":"Annals of Operations Research"},{"key":"4834_CR43","doi-asserted-by":"crossref","unstructured":"Tchuente, D., & Nyawa, S. (2021). Real estate price estimation in french cities using geocoding and machine learning. Annals of Operations Research, (pp. 1\u201338).","DOI":"10.1007\/s10479-021-03932-5"},{"key":"4834_CR44","unstructured":"Xia, H., Liu, J., & Zhang, Z.J. (2020). Identifying fintech risk through machine learning: analyzing the q &a text of an online loan investment platform. Annals of Operations Research, (pp. 1\u201321)."},{"key":"4834_CR45","doi-asserted-by":"crossref","unstructured":"Xiao, L., Wang, G., & Zuo, Y. (2018). Research on patent text classification based on word2vec and lstm. In 2018 11th international symposium on computational intelligence and design (ISCID), (vol. 1, pp. 71\u201374. IEEE).","DOI":"10.1109\/ISCID.2018.00023"},{"key":"4834_CR46","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., & Le, Q.V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems32."},{"key":"4834_CR47","first-page":"1229","volume":"40","author":"F Zhou","year":"2017","unstructured":"Zhou, F., Jin, L., & Dong, J. (2017). Review of convolutional neural network. Chinese Journal of Computers, 40, 1229\u20131251.","journal-title":"Chinese Journal of Computers"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-04834-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-022-04834-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-04834-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T18:28:25Z","timestamp":1722364105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-022-04834-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":47,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["4834"],"URL":"https:\/\/doi.org\/10.1007\/s10479-022-04834-w","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]},"assertion":[{"value":"10 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}