政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146897
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    题名: 應用於直播電商的探索性演員評論家推薦系統
    Exploratory Actor-Critic Recommender for Online Streaming Retailing
    作者: 簡巧恩
    贡献者: 林怡伶
    蕭舜文
    簡巧恩
    关键词: 推薦系統
    探索利用平衡
    強化學習
    深度學習
    streaming retailing
    actor-critic
    recommendation system
    deep reinforcement learning
    exploration
    日期: 2023
    上传时间: 2023-09-01 14:55:39 (UTC+8)
    摘要: 互動式推薦系統的發展受到了關注。此外，所有串流中提供的產品也都不同，這導致這些產品能夠在連續的行動空間中進行建模。因此，在線串流環境的大型物品空間中，我們使用了演員-評論家架構來推薦產品，以在用戶觀看直播時學習其偏好。基於演員生成的物品嵌入，我們選擇了最接近的幾個物品作為推薦的基礎。同時，為了確保用戶接收的信息足夠多樣，我們在演員生成結果嵌入之前提出了兩種探索策略。我們計劃進行相應的實驗，以檢驗所提出的探索策略是否能夠優於基線模型或一般的推薦系統。
    The development of interactive recommender systems has received atten tion. Besides, the products provided are different among all the streams plus, causing the products being able to be modeled in continuous action space. Therefore, the actor-critic architecture is used to recommend products in the large item space of online streaming environments to learn users’ preferences while watching live streams. Based on the item embedding generated by the actor, the closest few items are selected as the basis for the recommenda tion. At the same time, to ensure that the information received by users is sufficiently diverse, we proposed two exploration strategies before the actor generates the result embeddings. We planned to conduct corresponding ex periments to examine whether the proposed exploration strategies are able to outperform the baseline model or general recommenders.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    110356045
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110356045
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

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