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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/159088
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159088


    Title: 直播電商推薦框架:利用強化學習建模三方演化偏好
    A Recommendation Framework for Live-Streaming Commerce: Leveraging Reinforcement Learning to Model Tripartite Evolving Preference
    Authors: 林語恩
    Lin, Yu-En
    Contributors: 林怡伶
    蕭舜文

    Lin, Yi-Ling
    Hsiao, Shun-Wen

    林語恩
    Lin, Yu-En
    Keywords: 推薦系統
    直播電商
    強化學習
    演員評論家算法
    長短期偏好
    三方推薦
    Live-streaming commerce
    Actor-critic
    Recommendation system
    Deep reinforcement learning
    Long-term and short-term preference
    Tripartite recommendation
    Date: 2025
    Issue Date: 2025-09-01 15:03:29 (UTC+8)
    Abstract: 近年來,直播電商的商品交易總額持續攀升,推動線上購物從傳統的基於用戶與商品互動的靜態模式轉向更具動態性和社交沉浸感的體驗,進而重塑電子商務的發展模式。在直播電商平台上,顧客、直播主和商品進行即時互動,直播主透過其影響力在塑造顧客購買行為方面發揮著至關重要的作用。本篇論文提出了 TriRec-RL 推薦框架,這是一個結合顧客、商品和主播之間互動的推薦框架,能夠更好地捕捉直播電商中的動態用戶偏好。實驗結果表明 TriRec-RL 能夠有效地建模長期和短期偏好,且其性能優於其他現代的推薦模型。
    In recent years, the gross merchandise volume (GMV) of live-streaming e-commerce has steadily increased, shifting online shopping away from traditional static models based on user-item interactions toward a more dynamic and socially immersive experience, thereby reshaping the e-commerce landscape. On live-streaming e-commerce platforms, customers, streamers, and products engage in real-time interactions, where streamers play a crucial role in shaping customer purchasing behavior through their influence. This paper proposes the TriRec-RL recommendation framework, a recommendation framework that incorporates interactions among customers, products, and streamers to better capture dynamic user preferences in live-streaming e-commerce. Experimental results show that TriRec-RL outperforms state-of-the-art models by effectively modeling both long-term and short-term preferences.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    112356003
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112356003
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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