在商業領域中，對消費者(to Customer, 2C)端的企業而言，如何保留與取得具有價值的潛在客戶變得十分重要。隨著晶片技術遵循摩爾定律高速發展，各種新穎的人工智慧也孕育而生，如ChatGPT、Llama等大型語言模型，反而更加凸顯模型的「黑盒子」問題，也造就可解釋性人工智慧與機器學習的出現。
然而，商業領域的某部分發展卻相當緩慢，礙於成本的考量、無法跟上技術的更迭且無法信賴模型的預測結果，在規劃行銷策略取得潛在客戶名單的作業上，依然仰賴領域專家和決策者的經驗和領域知識，使得潛在客戶名單的人數無法突破限制。
綜上，本研究將提出一個通用的可解釋性框架，解決黑盒子問題與客戶名單人數不足的問題。該框架將透過大語言模型的基礎位元組對編碼(Byte-Pair-Encoding, BPE)對自然語言資料進行分詞，並將結果加入顧客資料中，成為特徵欄位，形成顧客畫像；接續由領域專家參與建立模型的過程，透過領域專家選擇與建議的特徵，採用深度優先搜尋額外獲得相似的特徵欄位，並將其作為機器學習模型的實際類別答案，最後進行分類任務與預測評比。
本研究提出的框架，在公開數據集的驗證結果中證明其有效性與泛化能力，且提出的潛在顧客人數比原始建模的做法多7.5倍；在個案研究中，則勝過專家基於過去經驗提出的客戶名單，此框架提出的客戶名單人數高於專家之2.4倍，回應率高於3.8倍，回應總人數則多9倍；更重要的是，建立模型的過程與預測結果皆具備領域知識的可解釋性，使企業可將經驗與知識轉移，為該產業的大型語言模型打下良好的基礎。

In the business sector, it is crucial for companies to acquire valuable potential customers. Recently, various innovative artificial intelligence platforms, such as large language models like ChatGPT have emerged. These developments have brought model opacity and the "black box" issue to the forefront, spurring the creation of explainable artificial intelligence and machine learning.
However, certain sectors within the business realm are evolving slowly, constrained by cost considerations, inability to keep pace with technological advancements, and distrust in the reliability of model predictions. The task of planning marketing strategies and generating lists of potential customers still predominantly relies on the experience and domain knowledge of experts and decision-makers, which limits the potential expansion of these customer lists.
This study proposes a universal, interpretable framework that addresses both the black-box nature of models and the inadequacy of customer list sizes. The framework utilizes Byte-Pair Encoding to segment any natural language data, which is then integrated as additional feature fields in customer data to create personas. The model development process engages domain experts who select and recommend features, employing a depth-first search to further identify similar feature fields. These fields serve as the actual class labels in machine learning models for classification tasks and performance evaluation.
This framework has been validated on public datasets, proving its effectiveness and generalizability. In case studies, it has outperformed expert-generated customer lists based on past experiences. Most importantly, the model building process and the predictive results are explainable in terms of domain knowledge, allowing for the transfer of experience and knowledge, and laying a robust foundation for large language models in this industry.

參考文獻

英文文獻
[1] A. Rakipi, O. Shurdi, and J. Imami, "Utilization of data mining and machine learning in digital and electronic payments in banks," (in English), Corporate and Business Strategy Review, vol. 4, no. 4, special issue, pp. 243-251, 2023, doi: 10.22495/cbsrv4i4siart5.
[2] H. Hassani, X. Huang, and E. Silva, "Digitalisation and Big Data Mining in Banking," (in English), Big Data and Cognitive Computing, vol. 2, no. 3, p. 18, 2018/07/20 2018, doi: 10.3390/bdcc2030018.
[3] M. Pagani and C. Pardo, "The impact of digital technology on relationships in a business network," (in English), Industrial Marketing Management, vol. 67, pp. 185-192, 2017/11 2017, doi: 10.1016/j.indmarman.2017.08.009.
[4] K. Pousttchi and M. Dehnert, "Exploring the digitalization impact on consumer decision-making in retail banking," (in English), Electronic Markets, vol. 28, no. 3, pp. 265-286, 2018/01/13 2018, doi: 10.1007/s12525-017-0283-0.
[5] P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, and P. M. Atkinson, "Explainable artificial intelligence: an analytical review," (in English), WIREs Data Mining and Knowledge Discovery, vol. 11, no. 5, 2021/07/12 2021, doi: 10.1002/widm.1424.
[6] G. P. Reddy and Y. V. P. Kumar, "Explainable AI (XAI): Explained," presented at the 2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 2023/04/27, 2023. [Online]. Available: http://dx.doi.org/10.1109/estream59056.2023.10134984.
[7] J. Achiam et al., "Gpt-4 technical report," (in English), arXiv preprint arXiv:2303.08774, 2023.
[8] H. Touvron et al., "Llama 2: Open foundation and fine-tuned chat models," (in English), arXiv preprint arXiv:2307.09288, 2023.
[9] M. AI. "Introducing LLaMA: A foundational, 65-billion-parameter language model." https://ai.meta.com/blog/large-language-model-llama-meta-ai/ (accessed 4, 2024).
[10] S. E. Spatharioti, D. M. Rothschild, D. G. Goldstein, and J. M. Hofman, "Comparing traditional and llm-based search for consumer choice: A randomized experiment," (in English), arXiv preprint arXiv:2307.03744, 2023.
[11] N. Ziems, W. Yu, Z. Zhang, and M. Jiang, "Large Language Models are Built-in Autoregressive Search Engines," presented at the Findings of the Association for Computational Linguistics: ACL 2023, 2023. [Online]. Available: http://dx.doi.org/10.18653/v1/2023.findings-acl.167.
[12] B. B. Arcila, "Is It a Platform? Is It a Search Engine? It's ChatGPT! The European Liability Regime for Large Language Models," (in English), J. Free Speech L., vol. 3, p. 455, 2023.
[13] B. Yao, M. Jiang, D. Yang, and J. Hu, "Empowering LLM-based machine translation with cultural awareness," (in English), arXiv preprint arXiv:2305.14328, 2023.
[14] M. Karpinska and M. Iyyer, "Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist," presented at the Proceedings of the Eighth Conference on Machine Translation, 2023. [Online]. Available: http://dx.doi.org/10.18653/v1/2023.wmt-1.41.
[15] R. Jain, N. Gervasoni, M. Ndhlovu, and S. Rawat, "A Code Centric Evaluation of C/C++ Vulnerability Datasets for Deep Learning Based Vulnerability Detection Techniques," presented at the 16th Innovations in Software Engineering Conference, 2023/02/23, 2023. [Online]. Available: http://dx.doi.org/10.1145/3578527.3578530.
[16] A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, "Large language models in medicine," (in English), Nature Medicine, vol. 29, no. 8, pp. 1930-1940, 2023/07/17 2023, doi: 10.1038/s41591-023-02448-8.
[17] S. Wu et al., "Bloomberggpt: A large language model for finance," (in English), arXiv preprint arXiv:2303.17564, 2023.
[18] A. B. Mbakwe, I. Lourentzou, L. A. Celi, O. J. Mechanic, and A. Dagan, "ChatGPT passing USMLE shines a spotlight on the flaws of medical education," (in English), PLOS Digit Health, vol. 2, no. 2, pp. e0000205-e0000205, 2023, doi: 10.1371/journal.pdig.0000205.
[19] N. Chiliya, G. Herbst, and M. Roberts-Lombard, "The impact of marketing strategies on profitability of small grocery shops in South African townships," (in English), African journal of business management, vol. 3, no. 3, p. 70, 2009.
[20] T. Damrongsakmethee and V.-E. Neagoe, "Data Mining and Machine Learning for Financial Analysis," (in English), Indian Journal of Science and Technology, vol. 10, no. 39, pp. 1-7, 2017/12/09 2017, doi: 10.17485/ijst/2017/v10i39/119861.
[21] E. R. Diaz Aditya and D. K. Yudha Satria, "Optimizing Bank Marketing Strategies Through Analysis Using Lightgbm," (in English), CoreID Journal, vol. 1, no. 2, pp. 58-65, 2023/07/30 2023, doi: 10.60005/coreid.v1i2.11.
[22] S. Shim, M. A. Eastlick, and S. Lotz, "Search-Purchase (S-P) Strategies of Multi-Channel Consumers," (in English), Journal of Marketing Channels, vol. 11, no. 2-3, pp. 33-54, 2004/03/15 2004, doi: 10.1300/j049v11n02_03.
[23] P. G. Patterson and T. Smith, "Relationship benefits in service industries: a replication in a Southeast Asian context," (in English), Journal of Services Marketing, vol. 15, no. 6, pp. 425-443, 2001/11/01 2001, doi: 10.1108/eum0000000006098.
[24] A. J. Faria and W. J. Wellington, "Validating business gaming: Business game conformity with PIMS findings," (in English), Simulation & Gaming, vol. 36, no. 2, pp. 259-273, 2005/06 2005, doi: 10.1177/1046878105275454.
[25] P. A. Chate, "Behavioral Modelling of Customer Marketing Patterns and Review Prediction Using Machine Learning Techniques," Dublin, National College of Ireland, 2022.
[26] M. A. Muslim, Y. Dasril, A. Alamsyah, and T. Mustaqim, "Bank predictions for prospective long-term deposit investors using machine learning LightGBM and SMOTE," (in English), Journal of Physics: Conference Series, vol. 1918, no. 4, p. 042143, 2021/06/01 2021, doi: 10.1088/1742-6596/1918/4/042143.
[27] E. Van den Broek, A. Sergeeva, and M. Huysman, "When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring," (in English), MIS quarterly, vol. 45, no. 3, 2021.
[28] T. Jovanov and M. Stojanovski, "Marketing knowledge and strategy for SMEs: Can they live without it?," (in English), Thematic Collection of papers of international significance:" Reengineering and entrepreneurship under the contemporary conditions of enterprise business", pp. 131-143, 2012.
[29] Y. Huang, M. Zhang, and Y. He, "Research on improved RFM customer segmentation model based on K-Means algorithm," presented at the 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), 2020/06, 2020. [Online]. Available: http://dx.doi.org/10.1109/iccia49625.2020.00012.
[30] E. Soares, P. Angelov, B. Costa, and M. Castro, "Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios," presented at the 2019 International Joint Conference on Neural Networks (IJCNN), 2019/07, 2019. [Online]. Available: http://dx.doi.org/10.1109/ijcnn.2019.8851842.
[31] R. Blanco and C. Lioma, "Graph-based term weighting for information retrieval," (in English), Information Retrieval, vol. 15, no. 1, pp. 54-92, 2011/06/28 2011, doi: 10.1007/s10791-011-9172-x.
[32] X. Xie, J. Niu, X. Liu, Z. Chen, S. Tang, and S. Yu, "A survey on incorporating domain knowledge into deep learning for medical image analysis," (in English), Medical Image Analysis, vol. 69, p. 101985, 2021/04 2021, doi: 10.1016/j.media.2021.101985.
[33] C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, "Integrating Machine Learning with Human Knowledge," (in English), iScience, vol. 23, no. 11, pp. 101656-101656, 2020, doi: 10.1016/j.isci.2020.101656.
[34] V. Belle and I. Papantonis, "Principles and Practice of Explainable Machine Learning," (in English), Front Big Data, vol. 4, pp. 688969-688969, 2021, doi: 10.3389/fdata.2021.688969.
[35] Q. Yu, "Research on Marketing Methods based on Machine Learning Model," (in English), Highlights in Business, Economics and Management, vol. 10, pp. 431-435, 2023/05/09 2023, doi: 10.54097/hbem.v10i.8135.
[36] W. Saeed and C. Omlin, "Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities," (in English), Knowledge-Based Systems, vol. 263, p. 110273, 2023/03 2023, doi: 10.1016/j.knosys.2023.110273.
[37] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," (in English), Advances in neural information processing systems, vol. 30, 2017.
[38] A. Barredo Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," (in English), Information Fusion, vol. 58, pp. 82-115, 2020/06 2020, doi: 10.1016/j.inffus.2019.12.012.
[39] R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, "Explainable Machine Learning for Scientific Insights and Discoveries," (in English), IEEE Access, vol. 8, pp. 42200-42216, 2020, doi: 10.1109/access.2020.2976199.
[40] A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," (in English), IEEE Access, vol. 6, pp. 52138-52160, 2018, doi: 10.1109/access.2018.2870052.
[41] L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, "Explaining Explanations: An Overview of Interpretability of Machine Learning," presented at the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018/10, 2018. [Online]. Available: http://dx.doi.org/10.1109/dsaa.2018.00018.
[42] G. Montavon, W. Samek, and K.-R. Müller, "Methods for interpreting and understanding deep neural networks," (in English), Digital Signal Processing, vol. 73, pp. 1-15, 2018/02 2018, doi: 10.1016/j.dsp.2017.10.011.
[43] S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. Pearson (in English), 2016.
[44] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press (in English), 2016.
[45] F. Pasquale, The black box society: The secret algorithms that control money and information. Harvard University Press (in English), 2015.
[46] C. Rudin, "Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead," (in English), Nat Mach Intell, vol. 1, no. 5, pp. 206-215, 2019, doi: 10.1038/s42256-019-0048-x.
[47] L. Zhang, S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey," (in English), Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1253, 2018.
[48] F. Doshi-Velez and B. Kim, "Considerations for Evaluation and Generalization in Interpretable Machine Learning," in The Springer Series on Challenges in Machine Learning, ed: Springer International Publishing, 2018, pp. 3-17.
[49] H. L. B. Chia, "The emergence and need for explainable AI," (in English), Advances in Engineering Innovation, vol. 3, no. 1, pp. 1-4, 2023/10/23 2023, doi: 10.54254/2977-3903/3/2023023.
[50] P. Q. Le, M. Nauta, V. B. Nguyen, S. Pathak, J. Schlötterer, and C. Seifert, "Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges," presented at the Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023/08, 2023. [Online]. Available: http://dx.doi.org/10.24963/ijcai.2023/747.
[51] A. Holzinger, "From Machine Learning to Explainable AI," presented at the 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), 2018/08, 2018. [Online]. Available: http://dx.doi.org/10.1109/disa.2018.8490530.
[52] B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, "Human-level concept learning through probabilistic program induction," (in English), Science, vol. 350, no. 6266, pp. 1332-1338, 2015/12/11 2015, doi: 10.1126/science.aab3050.
[53] Z. C. Lipton, "The Mythos of Model Interpretability," (in English), Queue, vol. 16, no. 3, pp. 31-57, 2018/06 2018, doi: 10.1145/3236386.3241340.
[54] C. Molnar, Interpretable machine learning. Lulu. com (in English), 2020.
[55] A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, "What do we need to build explainable AI systems for the medical domain?," (in English), arXiv preprint arXiv:1712.09923, 2017.
[56] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, "SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification," (in English), MedRxiv, p. 2020.04. 24.20078584, 2020.
[57] V. Couteaux, O. Nempont, G. Pizaine, and I. Bloch, "Towards interpretability of segmentation networks by analyzing deepdreams," in Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 9, 2019: Springer, pp. 56-63.
[58] F. L. D. Morais, A. C. B. Garcia, P. S. M. Dos Santos, and L. A. P. A. Ribeiro, "Do Explainable AI techniques effectively explain their rationale? A case study from the domain expert’s perspective," presented at the 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2023/05/24, 2023. [Online]. Available: http://dx.doi.org/10.1109/cscwd57460.2023.10152722.
[59] M. Junaid, S. Ali, F. Eid, S. El-Sappagh, and T. Abuhmed, "Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease," (in English), Computer Methods and Programs in Biomedicine, vol. 234, p. 107495, 2023/06 2023, doi: 10.1016/j.cmpb.2023.107495.
[60] J. Dressel and H. Farid, "The accuracy, fairness, and limits of predicting recidivism," (in English), Sci Adv, vol. 4, no. 1, pp. eaao5580-eaao5580, 2018, doi: 10.1126/sciadv.aao5580.
[61] K. Amarasinghe, K. T. Rodolfa, H. Lamba, and R. Ghani, "Explainable machine learning for public policy: Use cases, gaps, and research directions," (in English), Data & Policy, vol. 5, 2023, doi: 10.1017/dap.2023.2.
[62] A. Smith-Renner, R. Rua, and M. Colony, "Towards an Explainable Threat Detection Tool," in IUI workshops, 2019.
[63] S. Xie and P. S. Yu, "Next Generation Trustworthy Fraud Detection," presented at the 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), 2018/10, 2018. [Online]. Available: http://dx.doi.org/10.1109/cic.2018.00045.
[64] S. M. Mathews, "Explainable Artificial Intelligence Applications in NLP, Biomedical, and Malware Classification: A Literature Review," in Advances in Intelligent Systems and Computing, ed: Springer International Publishing, 2019, pp. 1269-1292.
[65] A. Das and P. Rad, "Opportunities and challenges in explainable artificial intelligence (xai): A survey," (in English), arXiv preprint arXiv:2006.11371, 2020.
[66] J. Stilgoe, "Who Killed Elaine Herzberg?," in Who’s Driving Innovation?, ed: Springer International Publishing, 2019, pp. 1-6.
[67] S. Murindanyi, B. Wycliff Mugalu, J. Nakatumba-Nabende, and G. Marvin, "Interpretable Machine Learning for Predicting Customer Churn in Retail Banking," presented at the 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 2023/04/11, 2023. [Online]. Available: http://dx.doi.org/10.1109/icoei56765.2023.10125859.
[68] T. Clement, N. Kemmerzell, M. Abdelaal, and M. Amberg, "XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process," (in English), Machine Learning and Knowledge Extraction, vol. 5, no. 1, pp. 78-108, 2023/01/11 2023, doi: 10.3390/make5010006.
[70] S. Technology. "STM Tech Trends 2024: FOCUS ON THE USER - Connect the dots." https://www.stm-assoc.org/standards-technology/stm-tech-trends-2024-focus-on-the-user-connect-the-dots/ (accessed 4, 2024).
[72] A. Cooper, "The Inmates are Running the Asylum," in Berichte des German Chapter of the ACM, ed: Vieweg+Teubner Verlag, 1999, pp. 17-17.
[73] A. Cooper, R. Reimann, and D. Cronin, About face 3: the essentials of interaction design. John Wiley & Sons (in English), 2007.
[74] A. Cooper, "The Origin of Personas. Retrieved June 12, 2008," ed, 2003.
[75] M. Kouprie and F. S. Visser, "A framework for empathy in design: stepping into and out of the user's life," (in English), Journal of Engineering Design, vol. 20, no. 5, pp. 437-448, 2009/10 2009, doi: 10.1080/09544820902875033.
[76] J. Pruitt and T. Adlin, The persona lifecycle: keeping people in mind throughout product design. Elsevier (in English), 2010.
[77] D. Matt. "Developing personas for marketing strategy." https://technomarketer.typepad.com/technomarketer/2008/04/developing-pers.html (accessed 4, 2024).
[78] Y.-n. Chang, Y.-k. Lim, and E. Stolterman, "Personas: from theory to practices," in Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges, 2008, pp. 439-442.
[79] R. Mccready. "23 User Persona Examples, Templates & Tips. ." https://venngage.com/blog/user-persona-examples/ (accessed 4, 2024).
[80] D. Travis. "How to create personas your design team will believe in." https://www.userfocus.co.uk/articles/personas.html (accessed 4, 2024).
[81] S. Blomkvist, "Persona–an overview," (in English), Retrieved November, vol. 22, no. 2004, p. 15, 2002.
[82] J. Porter. "Personas and the Advantage of Designing for Yourself." http://bokardo.com/archives/personas-and-the-advantage-ofdesigning-for-yourself/ (accessed 4, 2024).
[83] C. Perfetti. "Goal-Directed Design: An Interview with Kim Goodwin. ." http://www.uie.com/articles/goal_directed_design/ (accessed 4, 2024).
[84] R. Bonnie. "The Power of the Persona." https://www.pragmaticinstitute.com/resources/articles/product/the-power-of-the-persona/ (accessed 4, 2024).
[85] S. Zhao et al., "User profiling from their use of smartphone applications: A survey," Pervasive and Mobile Computing, vol. 59, p. 101052, 2019.
[89] M. Constantinides and J. Dowell, "A framework for interaction-driven user modeling of mobile news reading behaviour," in Proceedings of the 26th conference on user modeling, adaptation and personalization, 2018, pp. 33-41.
[90] W.-J. Lee, K.-J. Oh, C.-G. Lim, and H.-J. Choi, "User profile extraction from Twitter for personalized news recommendation," in 16th International conference on advanced communication technology, 2014: IEEE, pp. 779-783.
[91] M. A. Raghuram, K. Akshay, and K. Chandrasekaran, "Efficient User Profiling in Twitter Social Network Using Traditional Classifiers," in Advances in Intelligent Systems and Computing, ed: Springer International Publishing, 2015, pp. 399-411.
[92] Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang, "A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly," (in English), High-Confidence Computing, vol. 4, no. 2, p. 100211, 2024/06 2024, doi: 10.1016/j.hcc.2024.100211.
[93] K. Bostrom and G. Durrett, "Byte Pair Encoding is Suboptimal for Language Model Pretraining," presented at the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020. [Online]. Available: http://dx.doi.org/10.18653/v1/2020.findings-emnlp.414.
[94] P. Gage, "A new algorithm for data compression," (in English), The C Users Journal, vol. 12, no. 2, pp. 23-38, 1994.
[95] R. Sennrich, B. Haddow, and A. Birch, "Neural Machine Translation of Rare Words with Subword Units," presented at the Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016. [Online]. Available: http://dx.doi.org/10.18653/v1/p16-1162.
[96] J. Zhan et al., "An effective feature representation of web log data by leveraging byte pair encoding and TF-IDF," in Proceedings of the ACM Turing Celebration Conference-China, 2019, pp. 1-6.
[97] H. Face. "Summary of the tokenizers." https://huggingface.co/docs/transformers/tokenizer_summary#summary-of-the-tokenizers (accessed 4, 2024).
[98] thomwolf. "BPE tokenizers and spaces before words." https://discuss.huggingface.co/t/bpe-tokenizers-and-spaces-before-words/475 (accessed 4, 2024).
[99] A. Rai and S. Borah, "Study of Various Methods for Tokenization," in Applications of Internet of Things, ed: Springer Singapore, 2020, pp. 193-200.
[100] S. Junyi. "jieba." https://github.com/fxsjy/jieba (accessed 4, 2024).
[101] M. Schuster and K. Nakajima, "Japanese and Korean voice search," presented at the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012/03, 2012. [Online]. Available: http://dx.doi.org/10.1109/icassp.2012.6289079.
[102] T. Kudo, "Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates," presented at the Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018. [Online]. Available: http://dx.doi.org/10.18653/v1/p18-1007.
[103] T. Kudo and J. Richardson, "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing," presented at the Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2018. [Online]. Available: http://dx.doi.org/10.18653/v1/d18-2012.
[104] J. Jiang and J. Li, "Constructing financial sentimental factors in Chinese market using natural language processing," (in English), arXiv preprint arXiv:1809.08390, 2018.
[105] X. Li, Y. Meng, X. Sun, Q. Han, A. Yuan, and J. Li, "Is word segmentation necessary for deep learning of Chinese representations?," (in English), arXiv preprint arXiv:1905.05526, 2019.
[106] X. Song, A. Salcianu, Y. Song, D. Dopson, and D. Zhou, "Fast WordPiece Tokenization," presented at the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021. [Online]. Available: http://dx.doi.org/10.18653/v1/2021.emnlp-main.160.
[107] A. Vaswani et al., "Attention is all you need," (in English), Advances in neural information processing systems, vol. 30, 2017.
[108] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," (in English), arXiv preprint arXiv:1810.04805, 2018.
[109] H. Tsai, J. Riesa, M. Johnson, N. Arivazhagan, X. Li, and A. Archer, "Small and Practical BERT Models for Sequence Labeling," presented at the Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019. [Online]. Available: http://dx.doi.org/10.18653/v1/d19-1374.
[110] S. L. Scott, "Bayesian Methods for Hidden Markov Models," (in English), Journal of the American Statistical Association, vol. 97, no. 457, pp. 337-351, 2002/03 2002, doi: 10.1198/016214502753479464.
[111] D. Kumar and A. Thawani, "BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation," presented at the Proceedings of the Third Workshop on Insights from Negative Results in NLP, 2022. [Online]. Available: http://dx.doi.org/10.18653/v1/2022.insights-1.24.
[112] C. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard, and D. McClosky, "The Stanford CoreNLP Natural Language Processing Toolkit," presented at the Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014. [Online]. Available: http://dx.doi.org/10.3115/v1/p14-5010.
[113] X. Gutierrez-Vasques, C. Bentz, and T. Samardžić, "Languages Through the Looking Glass of BPE Compression," (in English), Computational Linguistics, vol. 49, no. 4, pp. 943-1001, 2023, doi: 10.1162/coli_a_00489.
[114] M. AI. "LLaMa." https://huggingface.co/docs/transformers/model_doc/llama2 (accessed 4, 2024).
[115] A. Das and R. M. Verma, "Can Machines Tell Stories? A Comparative Study of Deep Neural Language Models and Metrics," (in English), IEEE Access, vol. 8, pp. 181258-181292, 2020, doi: 10.1109/access.2020.3023421.
[116] B. Kim et al., "What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers," presented at the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021. [Online]. Available: http://dx.doi.org/10.18653/v1/2021.emnlp-main.274.
[117] S. Black et al., "GPT-NeoX-20B: An Open-Source Autoregressive Language Model," presented at the Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models, 2022. [Online]. Available: http://dx.doi.org/10.18653/v1/2022.bigscience-1.9.
[118] K. I. Roumeliotis and N. D. Tselikas, "ChatGPT and Open-AI Models: A Preliminary Review," (in English), Future Internet, vol. 15, no. 6, p. 192, 2023/05/26 2023, doi: 10.3390/fi15060192.
[119] O. AI. "Tiktoken." https://github.com/openai/tiktoken (accessed 4, 2024).
[120] O. AI. "What’s the GPT-4-Turbo encoding?" https://community.openai.com/t/whats-the-gpt-4-turbo-encoding/505059 (accessed 4, 2024).
[121] C.-J. Hsu, C.-L. Liu, F.-T. Liao, P.-C. Hsu, Y.-C. Chen, and D.-S. Shiu, "Breeze-7B Technical Report," (in English), arXiv preprint arXiv:2403.02712, 2024.
[122] V. Zouhar et al., "A Formal Perspective on Byte-Pair Encoding," presented at the Findings of the Association for Computational Linguistics: ACL 2023, 2023. [Online]. Available: http://dx.doi.org/10.18653/v1/2023.findings-acl.38.
[123] A. Amalia, O. S. Sitompul, T. Mantoro, and E. B. Nababan, "Morpheme Embedding for Bahasa Indonesia Using Modified Byte Pair Encoding," (in English), IEEE Access, vol. 9, pp. 155699-155710, 2021, doi: 10.1109/access.2021.3128439.
[124] A. M. Samin, "Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition," (in English), arXiv preprint arXiv:2401.15532, 2024.
[125] E. D. Emiru, S. Xiong, Y. Li, A. Fesseha, and M. Diallo, "Improving Amharic speech recognition system using connectionist temporal classification with attention model and phoneme-based byte-pair-encodings," (in English), Information, vol. 12, no. 2, p. 62, 2021.
[126] A. Araabi, C. Monz, and V. Niculae, "How effective is byte pair encoding for out-of-vocabulary words in neural machine translation?," (in English), arXiv preprint arXiv:2208.05225, 2022.
[127] T. Xu and P. Zhou, "Feature Extraction for Payload Classification: A Byte Pair Encoding Algorithm," presented at the 2022 IEEE 8th International Conference on Computer and Communications (ICCC), 2022/12/09, 2022. [Online]. Available: http://dx.doi.org/10.1109/iccc56324.2022.10065977.
[128] N. S. Sehwani, "No Features Needed," presented at the Proceedings of the 2022 ACM on International Workshop on Security and Privacy Analytics, 2022/04/18, 2022. [Online]. Available: http://dx.doi.org/10.1145/3510548.3519375.
[129] A. Dempster, F. Petitjean, and G. I. Webb, "ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels," (in English), Data Mining and Knowledge Discovery, vol. 34, no. 5, pp. 1454-1495, 2020/07/13 2020, doi: 10.1007/s10618-020-00701-z.
[130] N. Tavabi and K. Lerman, "Pattern Discovery in Physiological Data with Byte Pair Encoding," in Multimodal AI in Healthcare, ed: Springer International Publishing, 2022, pp. 227-243.
[131] M. Klymenko, S. M. Doesburg, G. Medvedev, P. Xi, U. Ribary, and V. A. Vakorin, "Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan," ed: Institute of Electrical and Electronics Engineers (IEEE), 2022.
[132] N. Fradet, N. Gutowski, F. Chhel, and J.-P. Briot, "Byte Pair Encoding for Symbolic Music," presented at the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023. [Online]. Available: http://dx.doi.org/10.18653/v1/2023.emnlp-main.123.
[133] X. Li and D. Fourches, "SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning," (in English), Journal of Chemical Information and Modeling, vol. 61, no. 4, pp. 1560-1569, 2021/03/15 2021, doi: 10.1021/acs.jcim.0c01127.
[134] S. Kawano, K. Yoshino, Y. Suzuki, and S. Nakamura, "Dialogue Act Classification in Reference Interview Using Convolutional Neural Network with Byte Pair Encoding," in Lecture Notes in Electrical Engineering, ed: Springer Singapore, 2019, pp. 17-25.
[135] H. Liu, "Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms," ed: MDPI AG, 2020.
[136] S. W. Golomb and L. D. Baumert, "Backtrack Programming," (in English), Journal of the ACM, vol. 12, no. 4, pp. 516-524, 1965/10 1965, doi: 10.1145/321296.321300.
[137] N. J. Nilsson, "Problem-solving methods in," (in English), Artificial Intelligence, vol. 5, 1971.
[138] N. J. Nilsson, "Principles of Artificial Intelligence," ed: Springer Berlin Heidelberg, 1982.
[139] F. Harary, "THE EXPLOSIVE GROWTH OF GRAPH THEORY," (in English), Annals of the New York Academy of Sciences, vol. 328, no. 1, pp. 5-11, 1979/06 1979, doi: 10.1111/j.1749-6632.1979.tb17762.x.
[140] R. Tarjan, "Depth-First Search and Linear Graph Algorithms," (in English), SIAM Journal on Computing, vol. 1, no. 2, pp. 146-160, 1972/06 1972, doi: 10.1137/0201010.
[141] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, "Introduction to algorithms (Fourth)," ed: The MIT Press Cambridge, Massachusett, 2022.
[142] C. Photphanloet and R. Lipikorn, "PM10 concentration forecast using modified depth-first search and supervised learning neural network," (in English), Science of The Total Environment, vol. 727, p. 138507, 2020/07 2020, doi: 10.1016/j.scitotenv.2020.138507.
[143] G. Holzmann, D. Peled, and M. Yannakakis, "On nested depth first search," in The SPIN Verification System, ed: American Mathematical Society, 1997, pp. 23-31.
[144] J. H. Reif, "Depth-first search is inherently sequential," (in English), Information Processing Letters, vol. 20, no. 5, pp. 229-234, 1985/06 1985, doi: 10.1016/0020-0190(85)90024-9.
[145] V. Palanisamy and S. Vijayanathan, "A Novel Agent Based Depth First Search Algorithm," presented at the 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020/10/30, 2020. [Online]. Available: http://dx.doi.org/10.1109/iccca49541.2020.9250826.
[146] Y.-H. Chen and C.-M. Wu, "An Improved Algorithm for Searching Maze Based on Depth-First Search," presented at the 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020/09/28, 2020. [Online]. Available: http://dx.doi.org/10.1109/icce-taiwan49838.2020.9258170.
[147] R. Poyiadzi, K. Sokol, R. Santos-Rodriguez, T. De Bie, and P. Flach, "FACE," presented at the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020/02/07, 2020. [Online]. Available: http://dx.doi.org/10.1145/3375627.3375850.
[148] Q. U. Ain, M. A. Chatti, K. G. C. Bakar, S. Joarder, and R. Alatrash, "Automatic Construction of Educational Knowledge Graphs: A Word Embedding-Based Approach," (in English), Information, vol. 14, no. 10, p. 526, 2023/09/27 2023, doi: 10.3390/info14100526.
[149] D. Q. Nguyen, V. Tong, D. Phung, and D. Q. Nguyen, "Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction," presented at the Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022/02/11, 2022. [Online]. Available: http://dx.doi.org/10.1145/3488560.3502183.
[150] S. Rahmani, S. M. Fakhrahmad, and M. H. Sadreddini, "Co-occurrence graph-based context adaptation: a new unsupervised approach to word sense disambiguation," (in English), Digital Scholarship in the Humanities, vol. 36, no. 2, pp. 449-471, 2020/04/13 2020, doi: 10.1093/llc/fqz048.
[151] A. A. Bichi, R. Samsudin, R. Hassan, L. R. A. Hasan, and A. Ado Rogo, "Graph-based extractive text summarization method for Hausa text," (in English), PLoS One, vol. 18, no. 5, pp. e0285376-e0285376, 2023, doi: 10.1371/journal.pone.0285376.
[152] Y. Du, F. Li, T. Zheng, and J. Li, "Fast Cascading Outage Screening Based on Deep Convolutional Neural Network and Depth-First Search," (in English), IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 2704-2715, 2020/07 2020, doi: 10.1109/tpwrs.2020.2969956.
[153] Q. Mei and M. Gül, "Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones," (in English), Structural Health Monitoring, vol. 19, no. 6, pp. 1726-1744, 2020/01/03 2020, doi: 10.1177/1475921719896813.
[154] A. Z. Syah, F. Helmiah, N. Irawati, and N. A. Hasibuan, "Depth first search algorithm in the expert system for diagnosis of palm oil growth obstacles," presented at the 4TH INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN MATERIALS SCIENCE AND ENGINEERING 2022, 2024. [Online]. Available: http://dx.doi.org/10.1063/5.0205513.
[155] I. U. Khan et al., "A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)," (in English), Sensors (Basel), vol. 22, no. 23, p. 9235, 2022, doi: 10.3390/s22239235.
[156] G. Logeswari, S. Bose, and T. Anitha, "An Intrusion Detection System for SDN Using Machine Learning," (in English), Intelligent Automation & Soft Computing, vol. 35, no. 1, pp. 867-880, 2023, doi: 10.32604/iasc.2023.026769.
[157] M. Wang, L. Yue, X. Yang, X. Wang, Y. Han, and B. Yu, "Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine," (in English), Biomedical Signal Processing and Control, vol. 68, p. 102630, 2021/07 2021, doi: 10.1016/j.bspc.2021.102630.
[158] Y. Xu, W. Cai, L. Wang, and T. Xie, "Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM," (in English), Shock and Vibration, vol. 2021, pp. 1-8, 2021/12/09 2021, doi: 10.1155/2021/1205473.
[159] E.-A. Minastireanu and G. Mesnita, "Light GBM Machine Learning Algorithm to Online Click Fraud Detection," (in English), Journal of Information Assurance & Cybersecurity, pp. 1-12, 2019/04/04 2019, doi: 10.5171/2019.263928.
[160] W. Cai, R. Wei, L. Xu, and X. Ding, "A method for modelling greenhouse temperature using gradient boost decision tree," (in English), Information Processing in Agriculture, vol. 9, no. 3, pp. 343-354, 2022/09 2022, doi: 10.1016/j.inpa.2021.08.004.
[161] Q. Li, Z. Wen, and B. He, "Practical Federated Gradient Boosting Decision Trees," (in English), Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4642-4649, 2020/04/03 2020, doi: 10.1609/aaai.v34i04.5895.
[162] G. e. Jiao and H. Xu, "Analysis and Comparison of Forecasting Algorithms for Telecom Customer Churn," (in English), Journal of Physics: Conference Series, vol. 1881, no. 3, p. 032061, 2021/04/01 2021, doi: 10.1088/1742-6596/1881/3/032061.
[163] G. Ke et al., "Lightgbm: A highly efficient gradient boosting decision tree," (in English), Advances in neural information processing systems, vol. 30, 2017.
[164] P. Septiana Rizky, R. Haiban Hirzi, and U. Hidayaturrohman, "Perbandingan Metode LightGBM dan XGBoost dalam Menangani Data dengan Kelas Tidak Seimbang," (in English), J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 15, no. 2, pp. 228-236, 2022/12/31 2022, doi: 10.36456/jstat.vol15.no2.a5548.
[165] J. Zhang, D. Mucs, U. Norinder, and F. Svensson, "LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets," (in English), Journal of Chemical Information and Modeling, vol. 59, no. 10, pp. 4150-4158, 2019/09/27 2019, doi: 10.1021/acs.jcim.9b00633.
[166] B. S. Wardani, S. Sa’adah, and D. Nurjanah, "Measuring and Mitigating Bias in Bank Customers Data with XGBoost, LightGBM, and Random Forest Algorithm," (in English), Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 1, pp. 142-155, 2023.
[167] Y. Hua, "An Efficient Traffic Classification Scheme Using Embedded Feature Selection and LightGBM," presented at the 2020 Information Communication Technologies Conference (ICTC), 2020/05, 2020. [Online]. Available: http://dx.doi.org/10.1109/ictc49638.2020.9123302.
[168] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," (in English), Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002.
[169] H. Zhang, L. Huang, C. Q. Wu, and Z. Li, "An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset," (in English), Computer Networks, vol. 177, p. 107315, 2020/08 2020, doi: 10.1016/j.comnet.2020.107315.
[170] S. Zhang, Y. Yuan, Z. Yao, J. Yang, X. Wang, and J. Tian, "Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm," (in English), Electronics, vol. 11, no. 9, p. 1495, 2022/05/06 2022, doi: 10.3390/electronics11091495.
[171] Y. Liu, Z. Yu, C. Chen, Y. Han, and B. Yu, "Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net," (in English), Analytical Biochemistry, vol. 609, p. 113903, 2020/11 2020, doi: 10.1016/j.ab.2020.113903.
[172] A. Gupta, A. Raghav, and S. Srivastava, "Comparative Study of Machine Learning Algorithms for Portuguese Bank Data," presented at the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2021/02/19, 2021. [Online]. Available: http://dx.doi.org/10.1109/icccis51004.2021.9397083.
[173] D. Ge, J. Gu, S. Chang, and J. Cai, "Credit Card Fraud Detection Using Lightgbm Model," presented at the 2020 International Conference on E-Commerce and Internet Technology (ECIT), 2020/04, 2020. [Online]. Available: http://dx.doi.org/10.1109/ecit50008.2020.00060.
[174] S. Zhang, Y. Hu, and Z. Tan, "Research on borrower's credit classification of P2P network loan based on LightGBM algorithm," (in English), International Journal of Embedded Systems, vol. 11, no. 5, p. 602, 2019, doi: 10.1504/ijes.2019.10024125.
[175] X. Ma, J. Sha, D. Wang, Y. Yu, Q. Yang, and X. Niu, "Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning," (in English), Electronic Commerce Research and Applications, vol. 31, pp. 24-39, 2018/09 2018, doi: 10.1016/j.elerap.2018.08.002.
[176] J. G. Ponsam, S. V. J. Bella Gracia, G. Geetha, S. Karpaselvi, and K. Nimala, "Credit Risk Analysis using LightGBM and a comparative study of popular algorithms," presented at the 2021 4th International Conference on Computing and Communications Technologies (ICCCT), 2021/12/16, 2021. [Online]. Available: http://dx.doi.org/10.1109/iccct53315.2021.9711896.
[177] P. Anitha and M. M. Patil, "RFM model for customer purchase behavior using K-Means algorithm," (in English), Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1785-1792, 2022/05 2022, doi: 10.1016/j.jksuci.2019.12.011.
[178] S. Monalisa, P. Nadya, and R. Novita, "Analysis for Customer Lifetime Value Categorization with RFM Model," (in English), Procedia Computer Science, vol. 161, pp. 834-840, 2019, doi: 10.1016/j.procs.2019.11.190.
[179] C. Yan, H. Sun, W. Liu, and J. Chen, "An integrated method based on hesitant fuzzy theory and RFM model to insurance customers’ segmentation and lifetime value determination," (in English), Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 159-169, 2018/07/27 2018, doi: 10.3233/jifs-169577.
[180] M. Song, X. Zhao, H. E, and Z. Ou, "Statistic-based CRM approach via time series segmenting RFM on large scale data," presented at the Proceedings of the 9th International Conference on Utility and Cloud Computing, 2016/12/06, 2016. [Online]. Available: http://dx.doi.org/10.1145/2996890.3007873.
[181] M. Mohammadzadeh, Z. Z. Hoseini, and H. Derafshi, "A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran," (in English), Procedia computer science, vol. 120, pp. 23-30, 2017, doi: 10.1016/j.procs.2017.11.206.
[182] V. Baradaran and M. Biglari, "Customer segmentation in Fast Moving Consumer Goods (FMCG) Industries by using developed RFM model," (in English), Journal of business management, vol. 7, no. 1, pp. 23-42, 2015.
[183] M. Maleki Minbashrazgah, A. Zarei, and Z. Hajiloo, "Identifying & Segmenting Key Customers for Prioritizing them Based on Lifetime Value using RFM Model (Case study: Internet customer of Qom Telecommunications Company)," (in English), Journal of Business Management, vol. 8, no. 2, pp. 461-478, 2016.
[184] J.-Y. Lee and H.-K. Lee, "Combined Response Modeling for Individual Marketing by RFM and Confidence," (in English), Journal of the Korean Data and Information Science Society, vol. 19, no. 2, pp. 597-608, 2008.
[185] Z. Zalaghi and Y. Abbasnejad Varzi, "Measuring customer loyalty using an extended RFM and clustering technique," (in English), Management Science Letters, vol. 4, no. 5, pp. 905-912, 2014, doi: 10.5267/j.msl.2014.3.026.
[186] S. Haghighatnia, N. Abdolvand, and S. Rajaee Harandi, "Evaluating discounts as a dimension of customer behavior analysis," (in English), Journal of Marketing Communications, vol. 24, no. 4, pp. 321-336, 2017/12/04 2017, doi: 10.1080/13527266.2017.1410210.
[187] R. D. Mehta, "Hyperparameter Tuning for the Prediction of Customer Revenue," Dublin, National College of Ireland, 2022.
[188] A. Mohan, "An Eclectic Approach for Predicting Customer Segmentation to Facilitate Market Basket Analysis," Dublin, National College of Ireland, 2022.
[189] Y. L. Wong, K. Madhavan, and N. Elmqvist, "Towards characterizing domain experts as a user group," in 2018 IEEE Evaluation and Beyond-Methodological Approaches for Visualization (BELIV), 2018: IEEE, pp. 1-10.
[190] P. J. Fadde and P. Sullivan, "Developing expertise and expert performance," (in English), Handbook of Research in Educational Communications and Technology: Learning Design, pp. 53-72, 2020.
[191] K. K. Premanand Chandrasekaran. "Domain-Driven Design with Java - A Practitioner's Guide: Create simple, elegant, and valuable software solutions for complex business problems." Packt Publishing. https://ddd-practitioners.com/home/glossary/domain-expert/ (accessed 6, 2024).
[192] Ž. Ð. Vujovic, "Classification Model Evaluation Metrics," (in English), International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, 2021, doi: 10.14569/ijacsa.2021.0120670.
[193] T. Saito and M. Rehmsmeier, "Basic evaluation measures from the confusion matrix," (in English), Access link: https://classeval. wordpress. com/introduction/basic-evaluation-measures, 2017.
[194] S. Narkhede, "Understanding confusion matrix," (in English), Towards Data Science, vol. 180, no. 1, pp. 1-12, 2018.
[195] A. Mishra, "Metrics to evaluate your machine learning algorithm," (in English), Towards data science, pp. 1-8, 2018.
[196] A. Albahr and M. Albahar, "An Empirical Comparison of Fake News Detection using different Machine Learning Algorithms," (in English), International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, 2020, doi: 10.14569/ijacsa.2020.0110917.
[199] J. KARKAVELRAJA. "Amazon Sales Dataset." https://www.kaggle.com/datasets/karkavelrajaj/amazon-sales-dataset (accessed 4, 2024).
[200] Lavanya. "Google Play Store Apps." https://www.kaggle.com/datasets/lava18/google-play-store-apps (accessed 4, 2024).
[201] P. Lokesh. "Amazon Products Sales Dataset 2023." https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset (accessed 4, 2024).
[202] D. Chen, "Online Retail. UCI Machine Learning Repository.," (in English), 2015, doi: https://doi.org/10.24432/C5BW33.
[206] A. Caramazza and J. R. Shelton, "Domain-Specific Knowledge Systems in the Brain: The Animate-Inanimate Distinction," (in English), Journal of Cognitive Neuroscience, vol. 10, no. 1, pp. 1-34, 1998/01 1998, doi: 10.1162/089892998563752.
[207] R. Abelson, "Differences between belief and knowledge systems," (in English), Cognitive Science, vol. 3, no. 4, pp. 355-366, 1979/10 1979, doi: 10.1016/s0364-0213(79)80013-0.
[208] V. Trigonakis et al., "{aDFS}: An Almost {Depth-First-Search} Distributed {Graph-Querying} System," in 2021 USENIX Annual Technical Conference (USENIX ATC 21), 2021, pp. 209-224.
[209] S. H. Nallamala, U. R. Bajjuri, S. Anandarao, D. D. D. Prasad, and D. P. Mishra, "A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems," (in English), IOP Conference Series: Materials Science and Engineering, vol. 981, no. 2, p. 022008, 2020/12/01 2020, doi: 10.1088/1757-899x/981/2/022008.
[210] X. Zhao et al., "Embedding in Recommender Systems: A Survey," (in English), arXiv preprint arXiv:2310.18608, 2023.
[211] W.-C. Yeh, "A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems," (in English), Expert Systems with Applications, vol. 36, no. 5, pp. 9192-9200, 2009/07 2009, doi: 10.1016/j.eswa.2008.12.024.
[212] W.-C. Yeh, Y.-P. Lin, Y.-C. Liang, C.-M. Lai, and C.-L. Huang, "Simplified swarm optimization for hyperparameters of convolutional neural networks," (in English), Computers & Industrial Engineering, vol. 177, p. 109076, 2023/03 2023, doi: 10.1016/j.cie.2023.109076.
[213] W.-C. Yeh, W. Zhu, Y.-F. Peng, and C.-L. Huang, "A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System," (in English), Applied Sciences, vol. 12, no. 20, p. 10595, 2022/10/20 2022, doi: 10.3390/app122010595.
[214] M. Wang, W.-C. Yeh, T.-C. Chu, X. Zhang, C.-L. Huang, and J. Yang, "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," (in English), Energies, vol. 11, no. 9, p. 2385, 2018/09/10 2018, doi: 10.3390/en11092385.
[215] N. de Vries et al., "The Race to Retain Healthcare Workers: A Systematic Review on Factors that Impact Retention of Nurses and Physicians in Hospitals," (in English), Inquiry, vol. 60, pp. 469580231159318-469580231159318, Jan-Dec 2023, doi: 10.1177/00469580231159318.
[216] E. Benevento, D. Aloini, and N. Squicciarini, "Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques," (in English), International Journal of Forecasting, vol. 39, no. 1, pp. 192-208, 2023/01 2023, doi: 10.1016/j.ijforecast.2021.10.006.

中文文獻
[69] 中國信息通信研究院. "大數據白皮書." http://www.caict.ac.cn/english/research/whitepapers/202303/P020230316608528378472.pdf (accessed 4, 2024).
[71] 向飒 and 杨媛媛, "基于用户画像的学术期刊精准化知识服务策略< sup>< xref ref-type=," (in Chinese), 科技与出版, vol. 40, no. 2, pp. 103-107, 2021.
[86] 徐天赐., "基于群体用户画像的手机银行理财产品个性化推荐系统的设计与实现.," 2021.
[87] 张宇敬, 王柳, 齐晓娜, 许美玲, and 王蕾, "基于信息熵的商业银行客户画像属性约简研究," (in Chinese), 河北大学学报 (自然科学版), vol. 42, no. 1, p. 98, 2022.
[88] 孟巍 et al., "基于大数据技术的电力用户画像," (in Chinese), 电信科学, no. S1, pp. 15-20, 2017.
[197] 簡榮宗. "個人資料保護法於銀行實務之運用." https://www.lawtw.com/archives/417428 (accessed 4, 2024).
[198] 莊植寧. "金融機構個人資料保護議題." https://kpmg.com/tw/zh/home/insights/2020/07/tw-personal-data-protection.html (accessed 4, 2024).
[203] 個人資料保護委員會籌備處. "個資法條文及相關解釋." https://pipa.pdpc.gov.tw/News_Laws_Detail.aspx?n=0F74A3ED48EE2C08&sms=C8F255C5F7FAE791&s=D49FB8C95D3D9604 (accessed 4, 2024).
[204] 金融監督管理委員會. "金管銀合字第10130002690 " https://law.fsc.gov.tw/LawContent.aspx?id=GL000624 (accessed 4, 2024).
[205] 金融監督管理委員會. "金融監督管理委員會指定非公務機關個人資料檔案安全維護辦法." https://law.moj.gov.tw/LawClass/LawAll.aspx?PCODE=G0380233 (accessed 4, 2024).