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系統識別號 U0002-1407201414463600
中文論文名稱 推薦系統應用於Email Flyers的商品選擇 以生鮮超市為例
英文論文名稱 Commodities Selection of Email Flyers by Recommender System: A Case of the Supermarket
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生中文姓名 張懿緯
研究生英文姓名 Yi-Wei Chang
學號 601410748
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2014-06-20
論文頁數 87頁
口試委員 指導教授-蔣璿東
委員-王鄭慈
委員-葛煥昭
委員-蔣璿東
中文關鍵字 推薦系統  協同過濾 
英文關鍵字 Recommender System  Collaborative Filtering 
學科別分類 學科別應用科學資訊工程
中文摘要 近年來電子商務的興起,人們的消費行為也逐漸在改變,從傳統的實體店面購物轉變為更便捷的網路電子購物,這種消費行為的改變使的傳統實體零售業(Retail)在銷售宣傳的模式上必須有所改變。由於Email Flyers(在台灣稱作Electronic Direct-Mail)的部分,成本較低廉,因此很多傳統實體零售業會藉由大量的發送電子促銷DM來吸引顧客回到門市進行消費,將最近的促銷品皆放入電子DM之中。就消費者行為而言,當顧客回到門市進行消費時,可能會購買不在預定購買清單或是電子DM中的商品,所以發送電子DM的主要目的是藉由DM吸引顧客回到門市進行消費。但電子DM中卻不一定都是顧客所喜好的商品,將過多種類的商品放入於電子DM以及發送次數過於頻繁時,將導致顧客必須使用相當多的時間來閱讀電子DM才能找到自己所喜好的商品;可能會導致顧客對於電子DM的觀感變差,產生厭惡感,因此無法吸引顧客回到門市進行消費。在本研究中,將利用協同過濾推薦系統的分析,設計一個適合以食品為主超市的推薦演算法,依據Cross-selling的概念,針對『顧客已經購買過的商品』和『顧客未購買過的商品』兩個因素進行考量,將顧客最有可能購買的商品製作成客製化電子DM,藉此吸引顧客回到門市進行消費。因此,本研究中所提出的推薦演算法除了能顧及顧客原有的喜好,藉此吸引顧客回到門市進行消費外,還能將更多顧客購買先前未購買過的商品推薦給顧客,以增加超市收益。
英文摘要 With the rise of e-commerce in recent years, most people have changed their purchase behavior. Instead of going to the physical stores shopping, people prefer to buy things online conveniently. These changes of purchase behavior causing traditional retail trade must change the way their advertising pattern. Due to the cheaper cost of email flyer (or electronic Direct-Mail), traditional retail trade would send lots of e-DM to attract customers to return back to physical retail store and put promotion merchandise into e-DM to attract customers to return to physical stores. When customers return to stores and buy merchandises, they probably would buy some merchandise that weren’t on the shopping list or on the e-DMs, therefore, the main purpose sending e-DM is attracting customers back to stores and purchase merchandises. However, not every merchandises on e-DM was customers’ favorite merchandise, putting too many kinds of merchandise or sending too many e-DMs would make customers spend too much time on finding their favorite merchandise on e-DMs. This might be leaving customers a bad impression, and stopped them from returning to the stores for shopping. In this paper, we would design a proper algorithm by analyzing Collaborative filtering recommender system for supermarket. According to the concept of cross-selling, we would consider these two factors, the merchandise that customers had bought and the merchandise that they hadn’t bought, and choose the most possible merchandise to make customize e-DM to attract customers return to store and purchase merchandise. Therefore, the algorithm we designed in this article could not only considering the customers’ purchase behavior to attract them return to store and purchase merchandise but also recommending the merchandise that customers hadn’t bought before to increase revenue for supermarket.
論文目次 目錄
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究架構 4
第2章 相關文獻與研究探討 5
2.1 以內容為基礎的推薦系統 6
2.2 協同過濾推薦系統 8
2.3 混合式推薦系統 17
第3章 研究流程 19
3.1 問題陳述 19
3.2 研究方法 26
第4章 實驗結果與探討 31
4.1 對所有顧客進行推薦 33
4.1.1 使用COS、BIG和CBF演算法 33
4.1.2 推薦新商品類別與重複購買相同商品類別分析 36
4.2 對目標顧客進行推薦 44
4.2.1 使用COS、BIG和CBF演算法 44
4.2.2 推薦新商品類別與重複購買相同商品類別分析 47
4.3 使用CBF組合演算法 52
4.3.1 對目標顧客進行推薦 52
4.3.2 對所有顧客進行推薦 62
第5章 結論與未來研究方向 68
參考文獻 71
附錄-英文論文 76

圖目錄
圖1 顧客對於商品的評價分數矩陣 10
圖2 Cos User-based計算範例 13
圖3 Cos Item-based計算範例 14
圖4 顧客商品評分表 20
圖5 Cosine Correlation Coefficient商品之間的相關性 20
圖6 顧客對於未購買過商品的相關性 21
圖7 預測顧客對於未購買過的商品可能會產生的評價 21
圖8 顧客交易紀錄 25
圖9 二元矩陣商品類別之間的相關性 25
圖10 Content-based Filtering Algorithm 30
圖11 所有顧客推薦4個商品類別的推薦成功率圖 34
圖12 所有顧客推薦5個商品類別的推薦成功率圖 34
圖13 所有顧客推薦6個商品類別的推薦成功率圖 35
圖14 所有顧客推薦4個商品類別的新商品類別推薦成功率圖 37
圖15 所有顧客推薦5個商品類別的新商品類別推薦成功率圖 37
圖16 所有顧客推薦6個商品類別的新商品類別推薦成功率圖 38
圖17 所有顧客推薦4個商品類別的重複購買相同商品類別推薦成功率圖 39
圖18 所有顧客推薦5個商品類別的重複購買相同商品類別推薦成功率圖 39
圖19 所有顧客推薦6個商品類別的重複購買相同商品類別推薦成功率圖 40
圖20 目標顧客推薦4個商品類別的推薦成功率圖 45
圖21 目標顧客推薦5個商品類別的推薦成功率圖 45
圖22 目標顧客推薦6個商品類別的推薦成功率圖 46
圖23 目標顧客推薦4個商品類別的新商品類別推薦成功率圖 47
圖24 目標顧客推薦5個商品類別的新商品類別推薦成功率圖 48
圖25 目標顧客推薦6個商品類別的新商品類別推薦成功率圖 48
圖26 目標顧客推薦4個商品類別的重複購買相同商品類別推薦成功率圖 49
圖27 目標顧客推薦5個商品類別的重複購買相同商品類別推薦成功率圖 50
圖28 目標顧客推薦6個商品類別的重複購買相同商品類別推薦成功率圖 50
圖29 目標顧客推薦4個商品類別的推薦成功率圖 54
圖30 目標顧客推薦5個商品類別的推薦成功率圖 54
圖31 目標顧客推薦6個商品類別的推薦成功率圖 55
圖32 目標顧客推薦4個商品類別的新商品類別推薦成功率圖 57
圖33 目標顧客推薦5個商品類別的新商品類別推薦成功率圖 57
圖34 目標顧客推薦6個商品類別的新商品類別推薦成功率圖 58
圖35 目標顧客推薦4個商品類別的重複購買相同商品類別推薦成功率圖 60
圖36 目標顧客推薦5個商品類別的重複購買相同商品類別推薦成功率圖 60
圖37 目標顧客推薦6個商品類別的重複購買相同商品類別推薦成功率圖 61
圖38 所有顧客推薦4個商品類別的推薦成功率圖 63
圖39 所有顧客推薦5個商品類別的推薦成功率圖 63
圖40 所有顧客推薦6個商品類別的推薦成功率圖 64
圖41 所有顧客推薦4個商品類別的新商品類別推薦成功率圖 64
圖42 所有顧客推薦5個商品類別的新商品類別推薦成功率圖 65
圖43 所有顧客推薦6個商品類別的新商品類別推薦成功率圖 65
圖44 所有顧客推薦4個商品類別的重複購買相同商品類別推薦成功率圖 66
圖45 所有顧客推薦5個商品類別的重複購買相同商品類別推薦成功率圖 66
圖46 所有顧客推薦6個商品類別的重複購買相同商品類別推薦成功率圖 67

表目錄
表1 以內容為基礎的推薦系統的主要問題表 7
表2 顧客交易紀錄2011年1月至8月 32
表3 商品類別銷售Top10統計圖(1與2月) 41
表4 商品類別銷售Top10統計圖(3與4月) 42
表5 商品類別銷售Top10統計圖(5與6月) 42
表6 商品類別銷售Top10統計圖(7與8月) 43
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