Browsing by Author "Liao, Hsiu-Yu"

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Churn prediction and social neighbour influences for different types of user groups in virtual worlds
(2019-06-01)
Liu, Duen-Ren
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Liao, Hsiu-Yu
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Chen, Kuan-Yu
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Chiu, Yi-Ling
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資訊管理與財務金融系註：原資管所+財金所
In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation-based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation-based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.
Customer Churn Prediction in Virtual Worlds
(2015)
Liao, Hsiu-Yu
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Chen, Luan-Yu
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Liu, Duen-Ren
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Chiu, Yi-Ling
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資訊管理與財務金融系註：原資管所+財金所
With the emerging of social network websites, more and more social network online games are booming. Players have more alternatives of VWs games, while platform providers suffer from the problems of high customer turnover rate and low-customer-loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is the best core marketing strategy. In this paper, we put emphasis on modeling a hybrid classification, which takes monetary cost, user behavior and social neighbor features into consideration. The experimental results show that the proposed hybrid model is well-suited for this problem in virtual worlds comparing to the existing churn prediction methods applied in the traditional retail, and financial industry.
Intelligent Power Resource Allocation by Context-Based Usage Mining
(2015)
Liao, Yu-Shan
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Liao, Hsiu-Yu
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Liu, Duen-Ren
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Fan, Wen-Ting
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Omar, Hani
The smart meter is an emerging appliance which can be used to collect and monitor the power consumption data on electrical devices. Users can monitor these devices in order to adjust their operations for energy saving results. To avoid wasting power, it is important to set a power usage constraint to effectively monitor and control users\' power consumption within a restricted power usage range. However, existing researches on smart-metering systems do not address this issue. In this study, a novel Intelligent Energy Saving (IES) system is proposed to effectively monitor and utilize the power usages of devices under the power consumption constraint. The IES system conducts context-based mining to discover device usage patterns and provide intelligent analysis of context-based usage behaviors, effective monitoring and utilization of power usages of devices for intelligent energy saving.
Product Recommendations based on the Bandwagon Effect of Virtual Houses in Virtual Worlds
(2016)
Chou, Yun-Cheng
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Chung, Chi-Ching
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Liao, Hsiu-Yu
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Liu, Duen-Ren
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資訊管理與財務金融系註：原資管所+財金所
With the rapid development of information technology, the virtual world platform has become a competitive market due to its prosperous future. Since the number of both users and virtual products has grown rapidly, recommender systems have come to play an important role in the virtual world in terms of solving information overload problems and obtaining benefits for both platform runners and users. In this paper, we argue that there are two important factors, social circle neighbors and the virtual house bandwagon effect, which affect users\' preferences for virtual products. Hence, we propose two novel recommendation approaches that predict users\' preferences based on an analysis of social influence between target user and his/her social circle neighbors, and the effect of the bandwagon phenomenon during the virtual house visit process respectively. The performance of the proposed approaches is evaluated by conducting experiments with a dataset collected from a virtual world platform in Taiwan. The experimental results show that the proposed approaches outperform the conventional recommendation methods, and also exhibit the effectiveness of turning to social influence and the bandwagon effect for improving recommendation accuracy in virtual worlds.
Recommender system based on social influence and the virtual house bandwagon effect in virtual worlds
(2018-01-01)
Liu, Duen-Ren
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Chou, Yun-Cheng
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Chung, Chi-Ching
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Liao, Hsiu-Yu
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資訊管理與財務金融系註：原資管所+財金所
Purpose Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors' influences and the virtual house bandwagon phenomenon, which affects users' preferences during their virtual house visits and purchasing processes. Design/methodology/approach The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users' preferences by combining the analyses of both factors. Findings The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations. Originality/value Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users' preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.
Virtual friend recommendations in virtual worlds
(2015-01-01)
Liao, Hsiu-Yu
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Chen, Kuan-Yu
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Liu, Duen-Ren
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Virtual worlds (VWs) are becoming effective interactive platforms in the fields of education, social sciences and humanities. Computing similarity among users is a technique commonly used to make friend recommendations in social networks. However, user communities in virtual worlds tenet 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 methods in virtual worlds. This study develops a virtual friend recommendation approach based on user similarity and contact strengths in virtual worlds. In the proposed 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. 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 that takes into account user similarity and contact strengths can elicit stronger prediction performance than the friend-classifier that considers only user similarity. Moreover, the proposed friend recommendation method outperforms the traditional friend of friend (FOF) method of friend recommendation in virtual worlds. (C) 2014 Elsevier B.V. All rights reserved.