Browsing by Author "Shih, YY"

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Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences
(2005-08-01)
Liu, DR
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Shih, YY
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommending products to attract customers and meet their needs is important in fiercely competitive environments. Recommender systems have emerged in e-commerce applications to Support the recommendation of products. Recently, a weighted RFM-based method (WRFM-based method) has been proposed to provide recommendations based on customer lifetime value, including Recency, Frequency and Monetary. Preference-based collaborative filtering (CF) typically makes recommendations based on the similarities of customer preferences. This study proposes two hybrid methods that exploit the merits of the WRFM-based method and the preference-based CF method to improve the quality of recommendations. Experiments are conducted to evaluate the quality of recommendations provided by the proposed methods, using a data set concerning the hardware retail marketing. The experimental results indicate that the proposed hybrid methods outperform the WRFM-based method and the preference-based CF method. (c) 2004 Elsevier Inc. All rights reserved.
Integrating AHP and data mining for product recommendation based on customer lifetime value
(2005-03-01)
Liu, DR
;
Shih, YY
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers' needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method. (C) 2004 Elsevier B.V. All rights reserved.
The use of a decomposed theory of planned behavior to study Internet banking in Taiwan
(2004)
Shih, YY
;
Fang, KT
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
With the liberalization and internalization of financial markets, in terms of the entrance of the World Trade Organization, banks in Taiwan face pressures in service quality and administrative efficiency. Predicting customers' intention to adopt Internet banking is an important issue. Attempts to understand how an individual's belief, embracing attitude, subjective norm an perceived behavioral control, can influence intention. Two versions of the model of the theory of planned behavior (TPB) - pure and decomposed - are examined and compared to the theory of reasoned action (TRA). Data are collected from approximately 425 respondents and structural equation modeling is used to analyze the responses. Results generally support TRA and TPB and provide a good fit to the data.