KIEE - The Transactions of the Korean Institute of Electrical Engineers

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S. Y. Hwang, S. H. Kim, 2012, An exploratory study of the electronic-CRM activities in tourism industry: The case of Travel agencies, Hotels, Airlines and Restaurants, Tourism Sciences, Vol. 32, No. 6, pp. 383-403

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C. Bull, 2003, Strategic issues in customer relationship management (CRM) implementation, Business Process Mgmt Journal, Vol. 9, No. 5, pp. 592-602

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P. Radoslav Kotorov, 2002, Ubiquitous organization: organizational design for e-CRM, Business Process Management Journal, Vol. 3, No. 3, pp. 218-232

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S. L. Pan, J. N. Lee, 2003, Using e-CRM for a unified view of the customer, Communications of the ACM, Vol. 46, No. 4, pp. 95-99

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Y. Cho, R. Hiltz, J. Fjermestad, 2002, An Analysis of Online Customer Complaints: Implications for Web Complaint Management, in Proceedings of the 35th Annual Hawaii International Conference of System Sciences, Vol. ieee, No. , pp. 2308-2317

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C. Voigt, C. S. Montero, M. Menichinelli, 2016, An empirically informed taxonomy for the Maker Movement, in Proc. of International Conference on Internet Science, Vol. springer, No. cham, pp. 189-204

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J. Grus, 2019, Data science from scratch: first principles with Python, O'Reilly Media, pp. 239-240

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C. Park, J. Kim, 2018, Trend Analysis in Maker Movement Using Text Mining, Korea Contents Association, Vol. 18, No. 12, pp. 468-488

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H. Lee, P. Kang, 2016, Analysis of reserch trends in technology management using topic modeling, Korean Institute of Industrial Engineers, Vol. 4, pp. 5208-5229

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T. Kim, H. Choi, H. Lee, 2016, A Study on the Research Trends in Fintech using Topic Modeling, Korea Academia Industrial cooperation Society, Vol. 17, No. 11, pp. 640-681

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S. Na, J. Kim, M. Jung, J. Ahn, 2016, Trend anlysis using topic modeling for simulation studies, Korea Society for Simulation, Vol. 25, No. 3, pp. 107-116

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J. L. Boyd-graber, D. M. Blei, 2009, Syntactic Topic Models, Advances in Neural Information Processing Systems, Vol. , No. , pp. 185-192

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X. Quan, C. Kit, Y. Ge, S. J. Pan, Jun 2015, Short and Sparse Text Topic Modeling via Self-Aggregation, in Proc. of the 24th International Joint Conference on Artificial Intelligence

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X. Yan, J. Guo, Y. Lan, X. Cheng, 2013, A biterm topic model for short texts, Proceedings of the 22nd International Conference on World Wide Web, ACM, pp. 1445-1456

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C. Li, H. Wang, Z. Zhang, A. Sun, 2016, Topic modeling for short texts with auxiliary word embeddings, Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 165-174

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A. Song, Y. Park, 2018, WV-BTM: A technique on improving accuracy of topic model for short texts in SNS, Digital Contents Society, Vol. 19, No. 1, pp. 51-58

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T. Mikolov, K. Chen, G. Corrado, J. Dean, 2013, Efficient Estimation of Word Representations in Vector Space, arXiv preprint arXiv, Vol. 1301, No. 3781

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T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, 2013, Distributed Representations of Words and Phrases and their Compositionality, Advances in Neural Information Processing Systems, pp. 3111-3119

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H. Kang, J. Yang, 2019, Optimization of Word2vec Models for Korean Word Embeddings, Digital Contents Society, Vol. 20, No. 4, pp. 825-833

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M. Claesen, B. De Moor, 2015, Hyperparameter search in machine learning, arXiv preprint arXiv:1502.02127

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Sepp Hochreiter, J. Schmidhuber, 1997, Long short-term memory, Neural computation, Vol. 9, No. 8, pp. 1735-1780

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Ashlee Vance, 2018, This man is the godfather the AI Community Wants to Forget, Bloomberg Businessweek

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T. Yun, C. Park, T. H, S. Kim, 2016, Malware Classification based on LSTM, Korea Information Science Society, Vol. 12, pp. 772-774

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Sepp Hochreiter, J. Schmidhuber, 1997, LSTM can solve hard long time lag problems, Advances in Neural Information Processing Systems, pp. 473-479

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Aurelien, 2018, Hands-On Machine Learning with Scikit-Learn & TensorFlow, O’REILLY, pp. 409-411

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Jesse Davis, aJesse Goadrich, 2006, The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd International Conference on Machine Learning, ACM, pp. 233-240

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers