{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T07:01:28Z","timestamp":1756191688632,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T00:00:00Z","timestamp":1555286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T00:00:00Z","timestamp":1555286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Electron Commer Res"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10660-019-09347-6","type":"journal-article","created":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T06:02:35Z","timestamp":1555308155000},"page":"245-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Determining user needs through abnormality detection and heterogeneous embedding of usage sequence"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4199-936X","authenticated-orcid":false,"given":"Younghoon","family":"Lee","sequence":"first","affiliation":[]},{"given":"Sungzoon","family":"Cho","sequence":"additional","affiliation":[]},{"given":"Jinhae","family":"Choi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,15]]},"reference":[{"issue":"1","key":"9347_CR1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.dss.2012.04.005","volume":"54","author":"AS Abrahams","year":"2012","unstructured":"Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87\u201397.","journal-title":"Decision Support Systems"},{"key":"9347_CR2","first-page":"6","volume":"4","author":"SP Algur","year":"2016","unstructured":"Algur, S. P., & Bhat, P. (2016). Abnormal web video detection using density based lof method. International Journal of Computer Sciences and Engineering, 4, 6\u201314.","journal-title":"International Journal of Computer Sciences and Engineering"},{"key":"9347_CR3","unstructured":"Amiriparian, S., Freitag, M., Cummins, N., & Schuller, B. (2017). Sequence to sequence autoencoders for unsupervised representation learning from audio. In Proceedings of the DCASE 2017 workshop."},{"key":"9347_CR4","volume-title":"Pattern recognition and machine learning","author":"Y Anzai","year":"2012","unstructured":"Anzai, Y. (2012). Pattern recognition and machine learning. Amsterdam: Elsevier."},{"key":"9347_CR5","doi-asserted-by":"crossref","unstructured":"Baeza-Yates, R., Jiang, D., Silvestri, F., & Harrison, B. (2015). Predicting the next app that you are going to use. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 285\u2013294). ACM.","DOI":"10.1145\/2684822.2685302"},{"issue":"2","key":"9347_CR6","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1002\/hfm.20140","volume":"19","author":"S Bahn","year":"2009","unstructured":"Bahn, S., Lee, C., Nam, C. S., & Yun, M. H. (2009). Incorporating affective customer needs for luxuriousness into product design attributes. Human Factors and Ergonomics in Manufacturing & Service Industries, 19(2), 105\u2013127.","journal-title":"Human Factors and Ergonomics in Manufacturing & Service Industries"},{"key":"9347_CR7","doi-asserted-by":"crossref","unstructured":"Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). Lof: Identifying density-based local outliers. In ACM sigmod record (Vol.\u00a029, pp. 93\u2013104). ACM.","DOI":"10.1145\/335191.335388"},{"issue":"4","key":"9347_CR8","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.ijresmar.2010.09.001","volume":"27","author":"R Decker","year":"2010","unstructured":"Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293\u2013307.","journal-title":"International Journal of Research in Marketing"},{"key":"9347_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N. V., & Swami, A. (2017). metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 135\u2013144). ACM.","DOI":"10.1145\/3097983.3098036"},{"key":"9347_CR10","doi-asserted-by":"crossref","unstructured":"Fang, Y., Si, L., Somasundaram, N., & Yu, Z. (2012). Mining contrastive opinions on political texts using cross-perspective topic model. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 63\u201372). ACM.","DOI":"10.1145\/2124295.2124306"},{"key":"9347_CR11","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1016\/j.jclepro.2016.05.039","volume":"135","author":"A Gabriel","year":"2016","unstructured":"Gabriel, A., Camargo, M., Monticolo, D., Boly, V., & Bourgault, M. (2016). Improving the idea selection process in creative workshops through contextualisation. Journal of cleaner production, 135, 1503\u20131513.","journal-title":"Journal of cleaner production"},{"key":"9347_CR12","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.csl.2016.06.005","volume":"53","author":"S Gerani","year":"2016","unstructured":"Gerani, S., Carenini, G., & Ng, R. T. (2016). Modeling content and structure for abstractive review summarization. Computer Speech & Language, 53, 302\u2013331.","journal-title":"Computer Speech & Language"},{"issue":"5","key":"9347_CR13","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1093\/poq\/nfl033","volume":"70","author":"RM Groves","year":"2006","unstructured":"Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646\u2013675.","journal-title":"Public Opinion Quarterly"},{"issue":"4","key":"9347_CR14","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1111\/poms.12043","volume":"23","author":"B Gu","year":"2014","unstructured":"Gu, B., & Ye, Q. (2014). First step in social media: Measuring the influence of online management responses on customer satisfaction. Production and Operations Management, 23(4), 570\u2013582.","journal-title":"Production and Operations Management"},{"key":"9347_CR15","doi-asserted-by":"crossref","unstructured":"Jang, B. R., Noh, Y., Lee, S. J., & Park, S. B. (2015). A combination of temporal and general preferences for app recommendation. In 2015 International conference on big data and smart computing (BigComp) (pp. 178\u2013185). IEEE.","DOI":"10.1109\/35021BIGCOMP.2015.7072829"},{"key":"9347_CR16","doi-asserted-by":"crossref","unstructured":"Jang, M., Seo, S., & Kang, P. (2018). Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. arXiv preprint arXiv:1802.03238.","DOI":"10.1016\/j.ins.2019.03.066"},{"key":"9347_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.engappai.2015.12.005","volume":"49","author":"J Jin","year":"2016","unstructured":"Jin, J., Ji, P., & Gu, R. (2016). Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence, 49, 61\u201373.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"9347_CR18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.engappai.2015.05.006","volume":"47","author":"J Jin","year":"2016","unstructured":"Jin, J., Ji, P., & Kwong, C. (2016). What makes consumers unsatisfied with your products: Review analysis at a fine-grained level. Engineering Applications of Artificial Intelligence, 47, 38\u201348.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"9347_CR19","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neucom.2017.05.046","volume":"266","author":"HK Kim","year":"2017","unstructured":"Kim, H. K., Kim, H., & Cho, S. (2017). Bag-of-concepts: Comprehending document representation through clustering words in distributed representation. Neurocomputing, 266, 336\u2013352.","journal-title":"Neurocomputing"},{"key":"9347_CR20","doi-asserted-by":"crossref","unstructured":"Kostakos, V., Ferreira, D., Goncalves, J., & Hosio, S. (2016). Modelling smartphone usage: A markov state transition model. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (pp. 486\u2013497). ACM.","DOI":"10.1145\/2971648.2971669"},{"key":"9347_CR21","doi-asserted-by":"crossref","unstructured":"Lappas, T., Crovella, M., & Terzi, E. (2012). Selecting a characteristic set of reviews. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 832\u2013840). ACM.","DOI":"10.1145\/2339530.2339663"},{"issue":"2\u20133","key":"9347_CR22","first-page":"167","volume":"17","author":"CC Lee","year":"2005","unstructured":"Lee, C. C., & Hu, C. (2005). Analyzing hotel customers\u2019 e-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing, 17(2\u20133), 167\u2013181.","journal-title":"Journal of Travel & Tourism Marketing"},{"key":"9347_CR23","unstructured":"Lee, T., & Bradlow, E. T. (2007). Automatic construction of conjoint attributes and levels from online customer reviews. University of Pennsylvania, The Wharton School Working Paper."},{"issue":"2","key":"9347_CR24","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.tele.2017.12.007","volume":"35","author":"Y Lee","year":"2018","unstructured":"Lee, Y., Park, I., Cho, S., & Choi, J. (2018). Smartphone user segmentation based on app usage sequence with neural networks. Telematics and Informatics, 35(2), 329\u2013339.","journal-title":"Telematics and Informatics"},{"key":"9347_CR25","doi-asserted-by":"crossref","unstructured":"Lin, J., Sugiyama, K., Kan, M. Y., & Chua, T. S. (2014). New and improved: Modeling versions to improve app recommendation. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 647\u2013656. ACM.","DOI":"10.1145\/2600428.2609560"},{"key":"9347_CR26","doi-asserted-by":"crossref","unstructured":"Liu, B., Kong, D., Cen, L., Gong, N.Z., Jin, H., & Xiong, H. (2015). Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 315\u2013324). ACM.","DOI":"10.1145\/2684822.2685322"},{"key":"9347_CR27","volume-title":"Hinextapp: A context-aware and adaptive framework for app prediction in mobile systems","author":"D Liu","year":"2018","unstructured":"Liu, D., Xiang, C., Li, S., Ren, J., Liu, R., Liang, L., et al. (2018). Hinextapp: A context-aware and adaptive framework for app prediction in mobile systems. In Sustainable computing: Informatics and systems."},{"key":"9347_CR28","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.tourman.2014.09.020","volume":"47","author":"Z Liu","year":"2015","unstructured":"Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140\u2013151.","journal-title":"Tourism Management"},{"key":"9347_CR29","doi-asserted-by":"crossref","unstructured":"Ly, D. K., Sugiyama, K., Lin, Z., & Kan, M. Y. (2011). Product review summarization from a deeper perspective. In Proceedings of the 11th annual international ACM\/IEEE joint conference on Digital libraries (pp. 311\u2013314). ACM.","DOI":"10.1145\/1998076.1998134"},{"key":"9347_CR30","doi-asserted-by":"crossref","unstructured":"Ma, Z., Sun, A., Yuan, Q., & Cong, G.(2012). Topic-driven reader comments summarization. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 265\u2013274). ACM.","DOI":"10.1145\/2396761.2396798"},{"key":"9347_CR31","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148."},{"key":"9347_CR32","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.dss.2016.06.013","volume":"89","author":"M Meire","year":"2016","unstructured":"Meire, M., Ballings, M., & Van den Poel, D. (2016). The added value of auxiliary data in sentiment analysis of facebook posts. Decision Support Systems, 89, 98\u2013112.","journal-title":"Decision Support Systems"},{"key":"9347_CR33","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., & Liu, B. (2012). Mining contentions from discussions and debates. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 841\u2013849). ACM.","DOI":"10.1145\/2339530.2339664"},{"key":"9347_CR34","doi-asserted-by":"crossref","unstructured":"Mukherji, A., Srinivasan, V., & Welbourne, E. (2014). Adding intelligence to your mobile device via on-device sequential pattern mining. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 1005\u20131014). ACM.","DOI":"10.1145\/2638728.2641285"},{"issue":"3","key":"9347_CR35","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1287\/mksc.1120.0713","volume":"31","author":"O Netzer","year":"2012","unstructured":"Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521\u2013543.","journal-title":"Marketing Science"},{"key":"9347_CR36","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","volume":"99","author":"MA Pimentel","year":"2014","unstructured":"Pimentel, M. A., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215\u2013249.","journal-title":"Signal Processing"},{"issue":"8","key":"9347_CR37","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1016\/j.im.2016.06.002","volume":"53","author":"J Qi","year":"2016","unstructured":"Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951\u2013963.","journal-title":"Information & Management"},{"key":"9347_CR38","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.dss.2015.10.006","volume":"81","author":"M Salehan","year":"2016","unstructured":"Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30\u201340.","journal-title":"Decision Support Systems"},{"issue":"3","key":"9347_CR39","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1108\/14678040510636748","volume":"6","author":"Y Satoh","year":"2005","unstructured":"Satoh, Y., Nagata, H., Kyt\u00f6m\u00e4ki, P., & Gerrard, S. (2005). Evaluation of the university library service quality: Analysis through focus group interviews. Performance Measurement and Metrics, 6(3), 183\u2013193.","journal-title":"Performance Measurement and Metrics"},{"key":"9347_CR40","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.dss.2016.05.010","volume":"88","author":"RP Schumaker","year":"2016","unstructured":"Schumaker, R. P., Jarmoszko, A. T., & Labedz, C. S, Jr. (2016). Predicting wins and spread in the premier league using a sentiment analysis of twitter. Decision Support Systems, 88, 76\u201384.","journal-title":"Decision Support Systems"},{"key":"9347_CR41","doi-asserted-by":"crossref","unstructured":"Shi, Y., Wei, F., Yu, K., & Wu, X. (2018). App usage prediction based on an extended feature selection method rspec mic. In 2018 IEEE 3rd international conference on big data analysis (ICBDA) (pp. 324\u2013328). IEEE.","DOI":"10.1109\/ICBDA.2018.8367701"},{"key":"9347_CR42","doi-asserted-by":"crossref","unstructured":"Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K. K., Xu, C., & Tapia, E. M. (2014). Mobileminer: Mining your frequent patterns on your phone. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 389\u2013400). ACM.","DOI":"10.1145\/2632048.2632052"},{"issue":"4","key":"9347_CR43","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1287\/mnsc.1110.1458","volume":"58","author":"M Sun","year":"2012","unstructured":"Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696\u2013707.","journal-title":"Management Science"},{"key":"9347_CR44","doi-asserted-by":"crossref","unstructured":"Tsaparas, P., Ntoulas, A., & Terzi, E.(2011). Selecting a comprehensive set of reviews. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168\u2013176). ACM.","DOI":"10.1145\/2020408.2020440"},{"key":"9347_CR45","unstructured":"Ulwick, A. W. (2003). The strategic role of customer requirements in innovation. Strategyn inc 13, 12."},{"issue":"2","key":"9347_CR46","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.destud.2012.08.003","volume":"34","author":"PA Verhaegen","year":"2013","unstructured":"Verhaegen, P. A., Vandevenne, D., Peeters, J., & Duflou, J. R. (2013). Refinements to the variety metric for idea evaluation. Design Studies, 34(2), 243\u2013263.","journal-title":"Design Studies"},{"key":"9347_CR47","doi-asserted-by":"crossref","unstructured":"Verma, P., Singh, P., & Yadava, R. (2017). Fuzzy c-means clustering based outlier detection for saw electronic nose. In 2017 2nd international conference for convergence in technology (I2CT) (pp. 513\u2013519). IEEE.","DOI":"10.1109\/I2CT.2017.8226182"},{"key":"9347_CR48","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ijhm.2014.10.013","volume":"44","author":"Z Xiang","year":"2015","unstructured":"Xiang, Z., Schwartz, Z., Gerdes, J. H, Jr., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120\u2013130.","journal-title":"International Journal of Hospitality Management"},{"key":"9347_CR49","doi-asserted-by":"crossref","unstructured":"Xu, X., Lei, Y., & Zhou, X. (2018). A lof-based method for abnormal segment detection in machinery condition monitoring. In 2018 Prognostics and system health management conference (PHM-Chongqing) (pp. 125\u2013128). IEEE.","DOI":"10.1109\/PHM-Chongqing.2018.00027"},{"issue":"6","key":"9347_CR50","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.ijinfomgt.2017.06.004","volume":"37","author":"X Xu","year":"2017","unstructured":"Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6), 673\u2013683.","journal-title":"International Journal of Information Management"},{"key":"9347_CR51","doi-asserted-by":"crossref","unstructured":"Yu, I. H., Song, J. J., Ko, J. M., Kim, Y. I. (2010). A survey of customer responses for developing value-added services. In 2010 international conference on control automation and systems (ICCAS) (pp. 815\u2013818). IEEE.","DOI":"10.1109\/ICCAS.2010.5670143"},{"issue":"4","key":"9347_CR52","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TCBB.2016.2561927","volume":"14","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Yin, Z., & Wang, R. (2017). Pattern classification of instantaneous cognitive task-load through gmm clustering, laplacian eigenmap, and ensemble svms. IEEE\/ACM Transactions on Computational Biology and Bioinformatics, 14(4), 947\u2013965.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"}],"container-title":["Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10660-019-09347-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10660-019-09347-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10660-019-09347-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T16:06:42Z","timestamp":1624982802000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10660-019-09347-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,15]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["9347"],"URL":"https:\/\/doi.org\/10.1007\/s10660-019-09347-6","relation":{},"ISSN":["1389-5753","1572-9362"],"issn-type":[{"type":"print","value":"1389-5753"},{"type":"electronic","value":"1572-9362"}],"subject":[],"published":{"date-parts":[[2019,4,15]]},"assertion":[{"value":"15 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}