本論文在地理性社群網路中，建構一個病毒式行銷顧客推薦系統，以找到影響力最大的用戶。目前，社群網路正以驚人的速度成長，訊息就像感冒病毒一樣，可以透過社群網路快速傳播。目前病毒式行銷顧客推薦系統，預測準確度不高，我們希望可以增強它來提高預測準確度。目前病毒式行銷顧客推薦系統只考量顧客在社群網路中的網路價值。而我們提出的增強式病毒式行銷顧客推薦系統，除了考量顧客的網路價值外，還增加考量了顧客的興趣、顧客對商家喜好度及顧客離商家距離，能更準確地找到影響力最大的用戶。最後以模擬證明，我們的方法可以提高預測準確度，為商家帶來較多的來客數。

This paper builds a viral marketing customer recommendation system in a location-based social network to find the most influential users. At present, social networks are growing rapidly, and messages are like a cold virus that can spread quickly through social networks. At present, the viral marketing customer recommendation system has low prediction accuracy, and we hope to enhance it to improve prediction accuracy. Currently, the viral marketing customer recommendation system only considers the customer's Network Value in the social network. The enhanced viral marketing customer recommendation system, in addition to considering the customer's Network Value, also increases considering the customer's interest, the customer's preference for the merchant and the customer's distance from the merchant, and can more accurately find the most influential users. Finally, the simulation proves that our method can improve the prediction accuracy and bring more visitors to the merchant.

目錄
第一章	前言...................................................................................................................1
1.1研究動機.....................................................................................................1
1.1.1目前病毒式行銷顧客推薦系統........................................................1
1.1.2為何需要病毒式行銷的顧客推薦系統？........................................2
1.1.3增強式病毒式行銷顧客推薦系統.................................. .................2
1.1.4應用範圍............................................................................................3
1.2影響因子.....................................................................................................4
1.2.1顧客的興趣........................................................................................4
1.2.2顧客對商家喜好度............................................................................4
1.2.3顧客離商家距離................................................................................5
1.3研究目標.....................................................................................................5
1.3.1增加預測的準確度.............................................................................5
1.3.2	系統評量............................................................................................5
1.4論文結構......................................................................................................6
第二章	文獻探討............................................................................................................7
2.1 Independent Cascade (IC) Model.................................................................7
2.2 顧客對商家喜好度......................................................................................8
2.2.1 Demographic Filtering...........................................................................9
2.2.2 Content-Based Filtering........................................................................10
2.2.3 Collaborative Filtering..........................................................................11
2.2.3.1 相關係數探討..........................................................................12
2.2.3.2  User-Based Collaborative Filtering........................................14
2.2.3.3  Item-Based Collaborative Filtering........................................15
2.3 顧客離商家距離........................................................................................16
第三章	研究方法...........................................................................................................18
3.1 系統架構....................................................................................................18
3.2 網路價值之計算........................................................................................19
3.3 顧客對商家喜好度之計算........................................................................22
3.4 顧客離商家距離之計算............................................................................23
3.5 最終網路價值計算....................................................................................24
3.6 折價券發放方式........................................................................................24
第四章	模擬結果...........................................................................................................25
4.1資料來源.....................................................................................................25
4.1.1 Foursquare 介紹................................................................................25
4.1.2 Foursquare Dataset.............................................................................26
4.2 模擬結果....................................................................................................27
4.2.1模擬設計與流程................................................................................27
4.2.2模擬結果分析....................................................................................27
第五章	結論...................................................................................................................31
5.1主要貢獻.....................................................................................................31
5.2本研究主要限制.........................................................................................31
5.3 未來研究方向............................................................................................31
5.4 總結............................................................................................................32
參考文獻...........................................................................................................................33
圖目錄
圖2.1、Independent Cascade (IC) Model......................................................................8
圖2.2、Demographic Filtering ......................................................................................9
圖2.3、Content-Based Filtering...................................................................................10
圖2.4、相關係數..........................................................................................................13
圖3.1、本研究之研究流程..........................................................................................18
圖4.1、Foursquare 介紹1...........................................................................................25 
圖4.2、Foursquare 介紹2...........................................................................................26
圖4.3、模擬一..............................................................................................................28
圖4.4、模擬二..............................................................................................................28
圖4.5、模擬三..............................................................................................................29
圖4.6、模擬四..............................................................................................................30



表目錄
表1.1、目前方法與改良式方法比較...........................................................................3
表2.1、用戶對商家評分表1.......................................................................................15
表2.2、用戶對商家評分表2.......................................................................................16
表3.1、 fred-wilson的好友資料.................................................................................20
表3.2、fred-wilson 一共去過的 9 家法式餐廳的打卡資料....................................21
表3.3、justin-shaffer一共去過的 11 家法式餐廳的打卡資料................................21
表3.4、justin-shaffer 被 fred-wilson 影響………………………………………….21
表3.5、fred-wilson 的網路價值……………………………………………………..21
表3.6、計算兩家餐廳相似度來預測未知評分……………………………………..23

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