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| 題名: | 深度學習為導向的可解釋性推薦系統-提升公部門的線上補助平台服務績效
Explainable Deep Learning - Based Recommendation Systems : Enhancing the Services of Public Sector Subsidy Online Platform |
| 作者: | 莊鈞諺
Zhuang, Jun-Yan |
| 貢獻者: | 胡毓忠
Hu, Yuh-Jong
莊鈞諺
Zhuang, Jun-Yan |
| 關鍵詞: | 可解釋性 AI
神經協同過濾
SHAP
個性化推薦系統
Explainable AI model
Neural Collaborative Filtering
SHAP
Personalized Recommendation System |
| 日期: | 2023 |
| 上傳時間: | 2023-09-01 15:39:58 (UTC+8) |
| 摘要: | 本研究旨在解決政府補助平台的使用效益不彰問題，透過個性化推薦系統協助台灣中小企業進行數位化轉型。為達此目標，研究者設計與訓練了一個基於深度學習的協同過濾分析推薦系統，此系統能夠根據使用者和推薦項目的多種特徵進行學習，並為使用者提供最可能感興趣的前五名產品推薦。
為強化模型的解釋性，研究者結合SHAP模組與深度學習模型，讓模型的預測結果更具透明度，並利用大型語言模型OpenAI的ChatGPT生成簡單易懂的語言解釋，協助使用者理解模型推薦的原因，進一步提升使用者的滿意度。
實際上線運行後的數據顯示，此組合的推薦系統明顯提升了平台使用效率，相較於僅依賴隨機推薦。總體來看，本研究的成果表明，結合深度學習的推薦系統、解釋性AI模組，以及語言模型生成的方式，可以有效地提升中小企業在政府資源發放平台上選擇雲端解決方案的效率與決策準確度，從而助力台灣中小企業的數位化轉型。
This research addresses inefficiencies in government subsidy platforms and aids SME digital transformation using a personalized recommendation system. We developed a collaborative filtering recommendation system based on deep learning. Empirical data shows significant efficiency improvements when combined with SHAP, an explainable AI module versus a random version.
The integration of SHAP enhances model interpretability, making predictions transparent. User-friendly language explanations, generated using a large language model (LLM), help users understand the system`s operation and boost satisfaction. The combined recommendation system with SHAP provides clear recommendation reasons, improving platform efficiency.
In conclusion, the blend of a deep learning-based recommendation system, explainable AI, and language model generation effectively enhances decision-making accuracy for cloud solutions on resource distribution platforms, aiding Taiwan`s SMEs` digital transformation. |
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| 描述: | 碩士
國立政治大學
資訊科學系碩士在職專班
110971009 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0110971009 |
| 資料類型: | thesis |
| 顯示於類別: | [資訊科學系碩士在職專班] 學位論文
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