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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84024

    Title: 電腦遊戲評量之擇優推薦系統;A selected recommender system based on computer game evaluation
    Authors: 連國佑;Lian, Guo-You
    Contributors: 資訊管理學系
    Keywords: 推薦系統;雅卡爾相似度;矩陣分解;協同過濾;混合式推薦系統
    Date: 2020-07-17
    Issue Date: 2020-09-02 17:56:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Steam平台內,擁有超過3萬多個遊戲,這些眾多推出的遊戲,而難以選擇符合自身喜好的遊戲,因此消費者若要尋找符合興趣喜好的遊戲,則需花費更多的時間進行查找。若能開發有效的推薦系統,更能使消費者容易觸及到符合各自喜好的遊戲,吸引消費者進行消費。
    ;here are more than 30,000 games on the Steam platform. These numerous games make it difficult for users to choose games that match their own preferences. Therefore, user need to spend more time to find games that match their preferences. If we can develop an effective recommendation system, user can easily reach games which users are interested in and also attract user to purchase.
    This study uses the steam platform dataset, combined with traditional user-based and model-based recommendation methods such as singular value decomposition (SVD) and non-negative matrix decomposition (NMF) method, designed and implement a selected recommender system based on computer game evaluation.
    For the use of research, we pre-processing the original data set, combining into user scoring matrix. Then the scoring matrix is used to train the two models, and the predictions of the two models are combined and selected optimized by different degrees of criteria. The last combined predictions produces mixed recommendation results. In the first experiment, we propose a method of choosing the best combination between the two models, so as to recommend the user. In the second experiment, we improved the method of first experiment, improved the original method and standard, and found that the result of second experiment was not much different from the overall result of first experiment, but the method proposed in second experiment allows users to access games that have not been touched.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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