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研究生:劉保鈞
研究生(外文):Liu bau jiun
論文名稱:以項目為基礎之多維度情境推薦
論文名稱(外文):Item-based Multidimensional Context Recommendation
指導教授:翁頌舜翁頌舜引用關係
學位類別:碩士
校院名稱:輔仁大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:70
中文關鍵詞:多維度協同過濾法推薦系統情境
外文關鍵詞:MultidimensionCollaborative FilteringRecommendation SystemContext
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由於網際網路的發展,造就了電子商務興盛,企業在這樣競爭激烈環境下必須提供更佳的服務或產品來確保使用者的忠誠,因此個人化與客製化的議題相應而生,個人化強調企業了解使用者的偏好進而提供符合他們喜好的商品,推薦系統就是根據這樣的概念而產生,目前推薦的方法可以分為內容導向法、協同過濾法、綜合推薦法三種,各種方法都有其利弊,但目前商業網站大多以項目為基礎(Item-based)之協同過濾法為主要應用的技術。以往Item-based協同過濾法作法主要將所有人看法彙整計算後進行推薦,但是這樣的機制忽略了某些特定群體的看法,故本研究將多維度概念導入Item-based協同過濾法中,希望考量多維度資訊找出符合某一特定群體看法的項目相似度,進而提高推薦準確性同時結合情境上的推薦讓使用者對於推薦系統所產生的結果更加滿意,本研究將Item-based協同過濾法與本研究所提出之多維度推薦法透過實際建置系統來比較兩者方法的好壞,最後實驗的結果證明導入多維度考量後推薦系統比Item-based協同過濾法更好。
The development of Internet arises in the popularity of E-Commerce at present. Under the competitive circumstance, corporations must offer better services and products to retain the customer’s loyalty. Based on what mentioned above, the issues related to personalization and customization become more and more popular. The term, personalization, emphasizes that the enterprise must find out what their customers prefer to and offer satisfying products to them. Thus, the recommendation systems which arise from the concept are used to solve the problem about information overloading, and etc. Recommendation methods can be classified into three categories such as Content-based Recommendation(CB), Collaborative Filtering Recommendation(CF), and Hybrid Recommendation. All of them have advantages and disadvantages. However, most commercial web sites take item-based collaborative filtering as major technique to serve customers. In the past, item-based collaborative filtering would recommend products to customers based on the collection of all people’s opinions. In this way, that would neglect the opinions of specific group of customers. Hence, our research incorporates multidimensional concept and context recommendation into item-based collaborative approach filtering approach to improve the accuracy of the item-based collaborative filtering and derive the more pleasing outcomes.
Besides, we compare the model we proposed with the item-based collaborative filtering approach. At last, the results of experiments show that the item-based multidimensional context recommendation is better than item-based recommendation.
圖次……………………………………………………………………………………………vi
表次……………………………………………………………………………………………vii
第壹章 研究動機與背景……………………………………………………………………1
第一節 研究背景……………………………………………………………………………1
第二節 研究目的……………………………………………………………………………2
第三節 研究流程……………………………………………………………………………2
第貳章 文獻探討……………………………………………………………………………5
第一節 推薦系統……………………………………………………………………………5
第二節 內容導向法(Content-based Approach) ……………………………………7
第三節 協同過濾法(Collaborative Filter Approach) …………………………9
第四節 綜合推薦法(Hybrid Recommendation) ……………………………12
第五節 相似度……………………………………………………………………15
第六節 預測評分計算……………………………………………………………17
第七節 多維度與情境推薦………………………………………………………17
第參章 研究方法…………………………………………………………………23
第一節 問題描述…………………………………………………………………23
第二節 研究範圍…………………………………………………………………23
第三節 研究架構…………………………………………………………………24
第四節 演算法……………………………………………………………………32
第五節 小結………………………………………………………………………34
第六節 範例說明…………………………………………………………………34
第肆章 實驗設計與結果分析……………………………………………………39
第一節 系統實作環境………….………………………………………………39
第二節 實驗系統架構與說明……………………………………………………40
第三節 實驗流程…………………………………………………………………48
第四節 維度切割比較……………………………………………………………53
第五節 實驗結果與分析…………………………………………………………54
第六節 個案分析…………………………………………………………………60
第七節 小結………………………………………………………………………61
第伍章 結論…………………………………………………………………………63
第一節 結論………………………………………………………………………63
第二節 建議與未來展望…………………………………………………………65
第三節 研究假設與限制…………………………………………………………66
參考文獻………………………………………………………………………………69
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