| dc.contributor.advisor | 楊建民 | zh_TW |
| dc.contributor.author (Authors) | 黃泓翔 | zh_TW |
| dc.creator (作者) | 黃泓翔 | zh_TW |
| dc.date (日期) | 2008 | en_US |
| dc.date.accessioned | 8-Apr-2010 16:29:07 (UTC+8) | - |
| dc.date.available | 8-Apr-2010 16:29:07 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-Apr-2010 16:29:07 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0095356018 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/38408 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理研究所 | zh_TW |
| dc.description (描述) | 95356018 | zh_TW |
| dc.description (描述) | 97 | zh_TW |
| dc.description.abstract (摘要) | 在人們的日常生活中，推薦是很普遍的一種社會行為，它使人們不必親自去體驗所有的事物，可透過別人的經驗來得知一件事情或商品的好或壞。隨著科技的快速發展與網際網路的普及，電子商務已逐漸的融入社會，成為人類生活中不可或缺的一部分。然而在網路上過量的資訊，使得個人在資訊的使用與搜尋上面臨極大的挑戰，更加刺激了對於推薦資訊的需求，因此許多推薦技術相繼提出，推薦系統也應運而生，不僅使得推薦的範圍擴大了，推薦的型態也更為豐富多元；同時，在近年電子商務的發展中，對於個人化與顧客導向服務的愈益重視，使得推薦系統逐漸成為一種必要的線上服務。在眾多的推薦技術之中，協同過濾推薦方法是最成功且最常被採用的推薦技術之一，許多台灣的拍賣平台上也都有採用類似概念的推薦系統，像是Yahoo!拍賣、露天拍賣上的評價機制均屬此類。然而，現行的拍賣評價機制都沒有採用社會網路的技術，本研究希望透過協同過濾與社會網路的結合，讓評價機制更趨於完備。本研究以台灣最大的拍賣網站Yahoo!為例，蒐集了44萬筆交易記錄，並以推薦網(ReferralWeb)系統的矩陣方法為基礎，找出人與商品的關係、商品與類別的關係、人與人的關係，建立起一個社會網路，讓使用者可查詢特定領域的專家，並與之交易。除此之外，也可直接詢問專家關於商品的資訊或購買技巧。透過這樣的機制，希望能降低消費者在購買商品時所產生的交易糾紛，讓人們在網路上的購物體驗能變得更好。 | zh_TW |
| dc.description.abstract (摘要) | Nowadays, recommendation is a common social behavior between people. People can evaluate things or commodities from others’ experience and opinions instead of their own experiences. Along with the development of technology and Internet today, E-commerce has become an indispensable part of human life. However, due to the overloaded information, people face a fantastic challenge when accessing and searching on the Internet. Therefore, many methods of recommendation were proposed, and systems of recommendation are to come with the tide of fashion. In addition, the development of E-commerce emphasized on personalization and customer-oriented services more in recent years, which make recommendation system becomes a necessary on-line service gradually.Collaborative Filtering is the most successful and adopted one in numerous recommendation methods. There are many auction platforms in Taiwan also use recommendation systems, such like "Yahoo Auction", "Ruten Auction", etc. However, the previous mentioned recommendation mechanisms haven’t used Social Network technology; this study will propose an recommendation system which combines Collaborative Filtering and Social Network technology.This research collects 440,000 transaction data from the Yahoo auction platform, which is the biggest auction website in Taiwan. Based on the matrix method of ReferralWeb system(Shah, 1997), this research would like to build up the matrix of relationships between Person-Commodity, Commodity-Category, and Person-Person. Based on the three matrixes, finally builds up a Social Network. In the Social Network, users can enquire experts refer to the specific category of commodity, and then refer to the shops which the experts like or directly ask them the commodity information and purchase skill. Relying on the mechanism proposed by this research, our goals are to reduce the transaction disputes arising from consumers purchase commodities, and to let people have better experiences in on-line shopping. | en_US |
| dc.description.tableofcontents | 謝辭 I摘要 II圖目錄 VII表目錄 VIII第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 3第三節 研究架構 4第二章 文獻探討 5第一節 推薦系統 5一、推薦系統應用架構 6二、推薦系統介面的呈現 9三、推薦系統的分類 10第二節 資訊過濾 11ㄧ、內容基礎過濾 11二、協同過濾 12三、經濟式過濾 13第三節 協同過濾 15一、協同過濾的定義 15二、協同過濾的運作機制 15三、協同過濾的相關系統 17四、協同過濾的優缺點 18第四節 社會網路 21一、社會網路分析 21二、社會網路分析的單元 21三、社會網路分析的種類 24四、推薦網(ReferralWeb) 26第五節 小結 26第三章 研究方法 27第一節 研究流程與系統架構 27一、推薦網(ReferralWeb)的系統流程 27二、本研究的系統流程與架構 29第二節 蒐集使用者交易與評價記錄 31一、買賣的人際關係 31二、商品與類別的關係 31三、資料蒐集方法 32四、資料蒐集量 32第三節 建立使用者社會關係網路 33一、人與人的關係假設 33二、人與人矩陣 33三、關係強度的定義 34四、人與人關係示意圖 34第四節 建立各類別的專家排行榜 36一、人與類別的關係假設 36二、人與類別矩陣 36三、專家強度的定義 37四、類別專家排行榜 37第五節 研究限制 38一、蒐集資料的先天限制 38二、實際蒐集資料所碰到的瓶頸 38第四章 系統建置與研究成果 39第一節 系統架構 39一、資料蒐集器 39二、分析引擎 40三、資料庫 40四、使用者介面 40第二節 資料的敘述統計 41一、熱門類別排行榜 41二、熱門類別的專家排行 41第三節 人與人的關係 43一、情境模擬 43二、人與人關係圖 43三、人與人的關係表 45四、小結 45第四節 關係鏈與類別的專家排行 46一、情境模擬 46二、人與專家之關係鏈 46三、類別的專家排行 47四、個人的興趣排行 48五、小結 48第五節 負評機制修正專家排行榜 49第六節 使用者輪廓 50第七節 小結 50第五章 結論、建議與未來展望 51第一節 結論與建議 51第二節 未來研究方向 52一、加速資料蒐集的速度 52二、自行定義“無類別商品”的類別 52三、全自動化的呈現與分析 53參考文獻 54 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0095356018 | en_US |
| dc.subject (關鍵詞) | 社會網路 | zh_TW |
| dc.subject (關鍵詞) | 推薦系統 | zh_TW |
| dc.subject (關鍵詞) | 協同過濾 | zh_TW |
| dc.subject (關鍵詞) | 專家推薦 | zh_TW |
| dc.subject (關鍵詞) | Social Network | en_US |
| dc.subject (關鍵詞) | Recommendation System | en_US |
| dc.subject (關鍵詞) | Collaborative Filtering | en_US |
| dc.subject (關鍵詞) | Expert Recommendation | en_US |
| dc.title (題名) | 基於社會網路的拍賣平台專家推薦系統之研究 | zh_TW |
| dc.type (資料類型) | thesis | en |
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