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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
;
Liao, Hsiu-Yu
;
Chen, Kuan-Yu
;
Chiu, Yi-Ling
;
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.
Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches
(2012-10-01)
Liu, Duen-Ren
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Lai, Chin-Hui
;
Chen, Ya-Ting
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods.
Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning
(2007-07-01)
Liu, Duen-Ren
;
Ke, Chih-Kun
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers' information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems. (c) 2006 Published by Elsevier Ltd.
Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations
(2008-01-01)
Liu, Duen-Ren
;
Ke, Chih-Kun
;
Lee, Jia-Yuan
;
Lee, Chun-Feng
;
資訊管理與財務金融系註：原資管所+財金所
Providing various c-services on the Internet by enterprises is an important trend in c-business. Composite e-services, which consist of various e-services provided by different e-service providers, are complex processes that require the cooperation among cross-organizational e-service providers. The flexibility and success of e-business depend on effective knowledge support to access related information resources of composite e-services. Thus, providing effective knowledge support for accessing composite e-services is a challenging task. This work proposes a knowledge map platform to provide an effective knowledge support for utilizing composite e-services. A data mining approach is applied to extract knowledge patterns from the usage records of composite e-services. Based on the mining result, topic maps are employed to construct the knowledge map. Meanwhile, the proposed knowledge map is integrated with recommendation capability to generate recommendations for composite e-services via data mining and collaborative filtering techniques. A prototype system is implemented to demonstrate the proposed platform. The proposed knowledge map enhanced with recommendation capability can provide users customized decision support to effectively utilize composite e-services. (c) 2006 Elsevier Ltd. All rights reserved.
Mining knowledge flow for modeling the information needs of task-based groups
(2008)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Knowledge is the most important resource to create core competitive advantages for an organization. Such knowledge is circulated and accumulated by a knowledge flow (KF) in an organization to support worker tasks. Workers may cooperate and participate in several task-based groups to fulfill their needs. In this paper, we propose a group-based knowledge flow mining algorithm which integrates information retrieval and data mining techniques for mining and constructing the group-based KF (GKF) for task-based groups. The GKF is expressed as a directed knowledge graph to represent the knowledge referencing behavior for a group of workers with similar task needs. The frequent knowledge referencing paths are identified from the knowledge graph to indicate the frequent knowledge flows of the workers. We also implement a prototype of GKF mining system to demonstrate the effectiveness of our proposed method.
Context-based knowledge support for problem-solving by rule-inference and case-based reasoning
(2008)
Liu, Duen-Ren
;
Ke, Chih-Kun
;
Wu, Mei-Yu
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problem. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situ ation-releva nt information based on the profiles.

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