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題名：虛擬世界之朋友推薦及客戶流失預測
作者：廖秀玉
校院名稱：國立交通大學
系所名稱：資訊管理研究所
指導教授：劉敦仁
學位類別：博士
出版日期：2015
主題關鍵詞：虛擬世界；社交網路；推薦系統；朋友推薦；客戶流失預測；資料探勘；影響力；Virtual World；Friend Recommendation；Customer Churn Prediction；Social Networks；Recommendation System；Social Influence；RFM Model；Data Mining
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虛擬世界是使用電腦圖片模擬現實世界，使用者藉由進行虛擬生活、社交互動與遊戲行為獲得自我提昇、自我滿足與紓解壓力，因此能吸引許多人踏入虛擬世界；虛擬世界網站經營公司，也因為有使用者以現實貨幣進行虛擬商品交易，得以支撐該虛擬世界公司的獲利與營運。透過這樣一個持續循環的生態圈，使用者與營運公司可以達到雙贏及蓬勃發展的局勢。
虛擬世界經歷如產品生命週期的初生期、成長期、成熟期及衰退期，因此虛擬世界的平台提供者亦需思考如何讓平台增加使用者及穩定使用者。使用者進入虛擬世界時，透過系統推薦朋友，讓虛擬朋友帶領他熟悉遊戲規則及環境，可以增進使用者和他人的互動頻率，活絡的互動及對環境產生安定感，有助於使用者對該虛擬世界的黏著度。使用者在虛擬世界藉著擁有的視覺化產品，和來往的朋友產生共同話題產生樂趣；在虛擬世界小額付款購買虛擬商品，同時為廠商帶來更高的利潤。隨著使用者熟悉虛擬世界之後，將慢慢面臨使用倦怠的衰退期甚至離開遊戲平台；同時，虛擬世界高度成長的市場吸引了許多遊戲廠商相繼投入資源研發，客戶缺發忠誠度及客戶流失變成重要的問題，因此預測客戶流失並採取對策是虛擬世界相關研究重要的議題。
本論文分析使用者的虛擬生活喜好特徵，探勘分析使用者互動模式及分解互動因子；根據互動因子分析使用者和相鄰使用者的互動強度，對於虛擬世界使用者提出有效的朋友推薦方法。針對虛擬世界客戶流失現象，先根據使用者與相鄰使用者的互動頻率分群，再分析使用者的虛擬生活行為變化、虛擬社交互動行為及使用者的生活圈鄰居流失，對不同客群造成的影響，根據不同族群的流失預測，得以協助虛擬世界平台者擬訂客戶忠誠計畫及行銷活動。以期透過此研究對虛擬世界平台者及使用者提出完整的推薦及預測系統，永續雙贏的正循環。
Virtual worlds (VWs) are becoming effective interactive platforms in the fields of education, social sciences and humanities. User communities in virtual worlds tend to have fewer real world linkages and more entertainment-related goals than those in social networks. The above characteristics result in an ineffective modality with respect to applying existing friend recommendation and customer churn prediction methods in virtual worlds. Firstly, this study develops a virtual friend recommendation approach based on user similarity and contact strengths in virtual worlds. Then, it proposes a customer churn prediction method taking users’ monetary cost, activity energy and social neighbor features into considerations.
In the proposed friend recommendation approach, users’ contact activities in virtual worlds are characterized into dynamic features and contact types to derive their contact strengths in communication-based, social-based, transaction-based, quest-based and relationship-based contact types. Classification approaches were developed to predict friend relationships based on user similarity and contact strengths among users. A novel friend recommendation approach is further developed herein to recommend friends as regards certain virtual worlds based on friend-classifiers. In the customer churn prediction approach, users are segmented into stable and unstable groups. Users’ consumption behaviors, virtual life and social life activity energy and social neighbors influence are analyzed by user segments. Different classification methods are applied to predict customer churn.
The evaluation uses mass data collected from an online virtual world in Taiwan, and validates the effectiveness of the proposed methodology. The experiment results show that the friend classifier and customer churn prediction that take into account contact strengths can elicit stronger prediction performance than the friend-classifier and churn prediction that considers only user similarity or monetary methods in the existing research.
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