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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105512

    Title: 考慮時間加權的圖書借閱推薦方法
    Other Titles: Time-weighted book recommendation method
    Authors: 黃種柏;Huang, Chung-Po
    Contributors: 淡江大學資訊管理學系碩士班
    魏世杰;Wei, Shih-Chieh
    Keywords: 圖書推薦;協同推薦;物推薦物;時間加權;Book Recommendation;Collaborative Recommendation;Item-to-Item Recommendation;Time-Weighting
    Date: 2015
    Issue Date: 2016-01-22 14:57:57 (UTC+8)
    Abstract: 一般提升推薦系統通常使用之方法,係利用用戶與商品的特徵,例如性別、年級、系所或是分類號等作分群以提升準確率,也有部分利用分群方法減少次序分析運算量,而較少著重交易發生時間點因素之考量。但一個人之興趣與行為,在實際與理論上的確會受時間影響。本文使用之借閱推薦方法考慮時間加權之因素,並為克服用戶身份識別問題,在過去研究的兩層式物推薦物方法基礎上,觀察時間因素在圖書借閱推薦中的影響效果。為了解決資料稀疏之問題,而且續借也是另一種形式之借出,故本文也將把續借視作一次借閱事件納入評估觀察,並探討各種評估條件組合的情況下時間加權之表現。之後納入用戶分群方法,針對不同用戶群強弱化調整其借閱事件權重,以觀察其效果。結果發現在各種不同條件組合之下,考慮時間加權的圖書推薦方法確實有較佳的精確率與召回率結果。在資料較不稀疏情況下,時間加權方法的表現提升更為顯著。而在利用分群法弱化狂熱群並強化一般群後,系統精確率表現也更佳。
    To enhance the performance of recommender systems, it is common to use the user or item features, such as gender, grades, departments or call numbers for clustering to improve the precision. Some works use clustering to reduce the computation time in sequence analysis. It is less often to find works taking the transaction time into account in recommendation. But in theory and practice personal interest and behavior often change with time. Based on a two-tier item-to-item collaborative recommendation method to accommodate the user identity problem, this work considers the time factor in book recommendation. To reduce the data sparsity problem, the renewal transaction is also regarded as a kind of checkout transaction. Then this work also considers user clustering to adjust the weight of borrow event based on different user groups. The experimental results show that under different combination of conditions the time weighted book recommendation method always performs better than the original method in precision and recall. On a less sparse data set, the time weighted book recommendation method also improves more significantly. When using the user clustering method to weaken the fanatic group and strengthen the normal group, the result also improves better in precision.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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