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研究生(外文):Yu-Kuo Lin
論文名稱(外文):A Valuable Cluster based Associative Recommender Mechanism for Online Community
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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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
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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