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研究生:林玉國
研究生(外文):Yu-Kuo Lin
論文名稱:以高價值群顧客為基之聯想式線上社群推薦機制
論文名稱(外文):A Valuable Cluster based Associative Recommender Mechanism for Online Community
指導教授:羅淑娟羅淑娟引用關係
口試委員:葉瑞徽林晶璟
口試日期:2007-06-12
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理研究所
學門:工程學門
學類:工業工程學類
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:57
中文關鍵詞:RFM模型群集分析推薦系統自組織映射圖網路(Self-Organizing MapSOM)霍普菲爾網路(Hopfield netHNN)
外文關鍵詞:RFMCluster AnalysisRecommender SystemSOMHNN algorithm
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現階段有企業編列大量的預算投入網路傳播媒體當中,雖然可以提高企業的知名度,但是卻吸引不了大多數的消費者消費,因為現代的消費者由於積極的交流訊息,因此已經不再完全相信企業傳來的資訊。實際影響消費者購買行為的主要關鍵為消費者之間的口耳相傳,因此,如果能夠將社群網路中較積極且具有價值的會員推薦給其他會員認識進而成為好友,相信藉由同儕團體的關係產生門檻效果、示範效果,會使得其他消費者的消費如滾雪球般的現象,另外,也能夠產生如店員般的會員給予消費者幫助。因此,本研究希望以業界真實資料做分析能夠有效的產生符合個人化推薦,並且使得往後企業透過本研究機制,能夠以較低的營運成本更有效的促進消費者消費。
本研究所提出的模組化推薦機制分為兩階段,第一階段為群集分析,以RFM為顧客特徵進行兩階段式群集分析,分析後找出具有意義的群集數,再將結果加以定義並且命名;在第二階段,本研究應用霍普菲爾網路做為推薦法則,利用第一階段所找到的VIP顧客群與具有潛力顧客群的留言關係做為訓練範例,以過去具有參考價值會員的交友風格做為典範來聯想欲推薦會員的交友風格。實驗結果得到推薦準確率為9.4%,搜全率為56.3%以及F1指標值為16.1%。
Nowadays, a great deal of firms are preparing tremendous budgets into internet media. It could not attract most consumers to purchase their products though it did make companies’ name famous. That is because today’s consumers communicate each other so actively that they don’t completely believe information from these firms. The key to affecting consumers’ purchasing behaviors would be the word-of-mouth advertising among consumers. As a result, if we can introduce those active and valuable members in community to others and make them become friends, consumptions will snowball as a demonstration by the relationship among colleagues. Moreover, these members will give consumers assistance just like sales clerks. Therefore, this research is to produce recommendation of customization by the analysis of actual business data. And then firms can use lower operation budgets to prompt consumers’ purchasing through this research system.
There are two steps of module recommender system in this research offered by our institute. The first step is cluster analyze. We proceed two-stage procedure cluster analyze of SOM+K-means for customers’ characters via RFM. After that we locate meaningful cluster numbers, defining the results and naming them. In the second step, this research applies Hopfield net as a recommendation principle, and uses the messages of VIP and potential customer groups in the first stage as a training demonstration. So we can infer recommended members’ friendship style from valuable members’ in the past. The results of this research are that precision is 9.4%, recall is 56.3% and F1 metric is 16.1%.
摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
第一章 序論 1
1.1 研究背景與動機 1
1.2 研究目的 4
第二章 文獻探討 6
2.1 RFM模型 6
2.1.1 RFM定義 6
2.1.2 RFM指標分數建構原則 7
2.2 群集分析 7
2.2.1 群集分析程序 8
2.2.2 群集分析衡量指標 10
2.2.3 群集分析研究發展與應用 11
2.3 個人化 12
2.3.1 個人化服務流程 13
2.3.2 個人化資訊的相關研究與應用 15
2.4 推薦系統 16
2.4.1 內容導向式推薦 17
2.4.2 協同式推薦 18
2.4.3 混合式推薦 19
2.4.4 推薦系統之評估方法 19
2.4.5 推薦系統之相關研究與應用 21
第三章 研究方法 24
3.1 研究架構 24
3.2 變數選擇 25
3.3 資料分析與前置處理 26
3.4 K-means Algorithm 27
3.5 自組織映射圖網路(Self-Organizing Map,SOM) 29
3.5.1 側向交互作用 29
3.5.2 網路架構 30
3.5.3 網路演算法 32
3.6 Two-Level Approach 35
3.7 霍普菲爾網路(Hopfield net,HNN) 35
第四章 實驗分析 39
4.1 資料分析 39
4.2 群集分析過程與結果 40
4.3 假設檢定 43
4.4 系統推薦 43
4.4.1 資料前置處理 43
4.4.2 推薦過程與結果 47
第五章 結論 51
5.1 成果與貢獻 51
5.2 未來研究方向 52
參考文獻 53
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