Friend Recommendation and Customer Churn Prediction in Virtual Worlds

• Main Author: 廖秀玉
• Other Authors: 劉敦仁
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/zq3hep

博士 === 國立交通大學 === 資訊管理研究所 === 103 === 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.