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    題名: 不同透明度下的人工智慧與群體電商推薦對比研究
    AI versus Crowd-Based E-Commerce Recommendations Under Different Levels of Transparency
    作者: 宋吟軒
    Sung, Yin-Hsuan
    貢獻者: 簡士鎰
    郁方
    Chien, Shih-Yi
    Yu, Fang
    宋吟軒
    Sung, Yin-Hsuan
    關鍵詞: 可解釋人工智慧
    透明度
    捷思法
    Explainable AI (XAI)
    Transparency
    Heuristics
    日期: 2024
    上傳時間: 2024-08-05 12:08:09 (UTC+8)
    摘要: 此研究探討捷思法機制和透明度在電子商務情境下對產品推薦接受度的影響。人工智慧推薦採用 SASRec 模型並使用 Kernel SHAP 作為 XAI 技術來解釋模型預測，用來激發機器捷思法。另一方面，群眾推薦透過展示推薦產品的銷售排名和星級評分來觸發從眾捷思法。Situation Awareness-Based Agent Transparency（SAT）模型則被用來創建三種不同級別的模型透明度。我們進行三輪前導試驗以驗證我們的界面設計在表現不同推薦方面的有效性。使用者研究結果（N=45）顯示在 SAT-1 和 SAT-1+2 條件下受測者偏好群眾推薦。雖然在三種透明度級別間沒有觀察到顯著差異，但我們在平均值中發現一些趨勢。具體而言，人工智慧推薦在 SAT-1+2+3 下擁有最高的平均接受度，而群眾推薦在 SAT-1+2 下最被接受。這些發現表明，消費者在中等透明度下偏好群眾推薦，但在更高透明度下群眾推薦的局限性資訊反而引發使用者更系統性的思考。我們的結果為電子商務平台提供實務上的見解，平台可以優先使用群眾推薦，並利用銷售排名和星級評分來觸發從眾捷思法，從而解決用戶冷啟動問題。隨著消費者數據增加，平台則可轉向採用人工智慧推薦，提供推薦背後的原因及侷限性資訊以突顯系統能力，從而提升消費者接受度，解決產品冷啟動問題。
    This study investigates the influence of heuristic mechanisms and transparency on accepting product recommendations in e-commerce contexts. AI-based recommendations utilize the SASRec model and incorporate Kernel SHAP as XAI techniques to explain the model predictions, stimulating machine heuristics. In contrast, crowd-based recommendations trigger bandwagon heuristics by presenting recommended products’ sales rank and star ratings. The Situation Awareness-Based Agent Transparency (SAT) model is used to create three levels of model transparency. We conducted three pilot tests to validate the effectiveness of our interface designs for representing different recommendations. The user study results (N=45) indicate that participants favor crowd-based recommendations, particularly under SAT-1 and SAT-1+2 conditions. Although no significant differences were observed across the three transparency levels, some trends emerged in the mean values. Specifically, AI-based recommendations showed the highest mean acceptance under SAT-1+2+3, while crowd-based recommendations were most accepted under SAT-1+2. These findings suggest that while consumers may favor crowd-based recommendations under moderate transparency, the limitations of crowd-based recommendations prompt more systematic processing under higher transparency levels. Our results offer practical insights for e-commerce platforms to prioritize crowd-based recommendations for new users by leveraging sales rank and star ratings to stimulate consumers’ bandwagon heuristics, thus addressing the user cold start problem. As more consumer data becomes available, platforms can transition to AI-based recommendations to effectively tackle the item cold start problem, providing additional explanations about the reasoning behind the recommendations and including limitation information to highlight the system’s capabilities, thereby enhancing consumer acceptance.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    111356050
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111356050
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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