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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/153164


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    Title: 通過人在迴圈和可解釋性人工智慧提升推薦系統
    Advancing Recommendation Systems through Human-in-the-Loop and Explainable AI
    Authors: 陳彥維
    Chen, Yen-Wei
    Contributors: 簡士鎰
    郁方
    Chien, Shih-Yi
    Yu, Fang
    陳彥維
    Chen, Yen-Wei
    Keywords: 推薦系統
    人在迴圈
    可解釋人工智慧
    Recommendation System
    Human in the loop
    Explainable AI
    Date: 2024
    Issue Date: 2024-09-04 14:06:33 (UTC+8)
    Abstract: 推薦系統已成為日常生活中的重要組成部分，然而人工智慧的引入卻帶來了“黑箱”挑戰。個性化也是一個關鍵問題。本研究旨在從人機互動的角度支持推薦系統，利用可解釋人工智慧來闡明人工智慧的決策過程，並採用基於情境感知的代理透明度模型來增強清晰度。此外，研究中還納入了“人在迴圈”的概念，結合人類反饋以顯著提高系統的滿意度和個性化。實證表明，將人工智慧和人在迴圈整合到推薦系統中，不僅可以提高推薦的信任度，也提高了用戶的使用意圖，並在透過持續的反饋，能夠在動態環境中保持優秀的性能。但在整合的同時，也要注意過度透明度所帶來的負面影響。
    Recommendation systems have become an integral part of daily life, yet the introduction of AI presents “black box" challenges. Personalization is also a critical issue. This study aims to support recommendation systems from a human-computer interaction perspective by utilizing Explainable AI (XAI) to elucidate AI's decision-making process and adopting an agent transparency model based on situational awareness to enhance clarity. Additionally, the study incorporates the concept of “humans in the loop", integrating human feedback to significantly improve system satisfaction and personalization. Empirical evidence indicates that integrating XAI and HITL into recommendation systems not only enhances the trustworthiness of recommendations but also increases users' intention to use the system. Through continuous feedback, the system can maintain excellent performance in dynamic environments. However, it is essential to be mindful of the potential negative impacts of excessive transparency during integration.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    111356046
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356046
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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