Duen-Ren Liu

Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences

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.

Discovering competitive intelligence by mining changes in patent trends

Obtaining sufficient competitive intelligence is a critical factor in helping business managers gain and maintain competitive advantages. Patent data is an important source of competitive intelligence that enterprises can use to gain a strategic advantage. Under existing approaches, to detect changes in patent trends, business managers must rely on patent analysts to compare two patent analysis charts of different time periods. The discovery of change of trends currently still needs laborious human efforts and no efficient computer-based approaches are available for helping this task. In this paper, we propose a patent trend change mining (PTCM) approach that can identify changes in patent trends without the need for specialist knowledge. The proposed approach consists of steps including patent collection, patent indicator calculation, and change detection. In change detection phase the approach firstly excavate rules between two different time periods, comparing them to determine the trend changes. These trend changes are then classified into four categories of change, evaluated with change degree and ranked by their change degree as the output information to be referred by decision makers. We apply the PTCM approach to Taiwans semiconductor industry to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, company activities in the industry and industry activities generally. The proposed approach generates competitive intelligence to help managers develop appropriate business strategies.

Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands

Recommender systems are techniques that allow companies to develop one-to-one marketing strategies and provide support in connecting with customers for e-commerce. There exist various recommendation techniques, including collaborative filtering (CF), content-based filtering, WRFM-based method, and hybrid methods. The CF method generally utilizes past purchasing preferences to determine recommendations to a target customer based on the opinions of other similar customers. The WRFM-based method makes recommendations based on weighted customer lifetime value - Recency, Frequency and Monetary. This work proposes to use customer demands derived from frequently purchased products in each industry as valuable information for making recommendations. Different from conventional CF techniques, this work uses extended preferences derived by combining customer demands and past purchasing preferences to identify similar customers. Accordingly, this work proposes several hybrid recommendation approaches that combine collaborative filtering, WRFM-based method, and extended preferences. The proposed approaches further utilize customer demands to adjust the ranking of recommended products to improve recommendation quality. The experimental results show that the proposed methods perform better than several other recommendation methods.

Multi-criteria task assignment in workflow management systems

During workflow design, workflow modelers generally specify the performers of a task by their organizational role. However, during workflow enactment, numerous workers with different skills and expertise may share the same role in an organization, making it hard to select appropriate individuals based merely on the assignment relation between a role and a task. To bridge the gap between abstract roles and real workers, this work proposes a multi-criteria assessment model capable of evaluating the suitability of individual workers for a specified task according to their capabilities, social relationships, and existing tasks. Candidates are ranked based on their suitability scores to support workflow administrators in selecting appropriate workers to perform the tasks assigned to a given role. The proposed assessment model overcomes the lack of role-based task assignment in current workflow management systems.

Business-to-business workflow interoperation based on process-views

When cooperating with each other, enterprises must closely monitor internal processes and those of partners to streamline business-to-business (B2B) workflows. This work applies the process-view model, which extends beyond conventional activity-based process models, to design workflows across multiple enterprises. A process-view is an abstraction of an implemented process. An enterprise can design various process-views for different partners based on diverse commercial relationships and, in doing so, establish an integrated process that consists of internal processes and process-views that each partner provides. Participatory enterprises can obtain appropriate progress information from their own integrated processes, allowing them to collaborate effectively. Furthermore, B2B workflows are coordinated through virtual states of process-views. This work develops a uniform approach to manage state mappings between internal processes and process-views. The proposed approach enhances prevalent activity-based process models adaptable to collaborative environments.

Rough set-based logics for multicriteria decision analysis

In this paper, we propose some decision logic languages for rule representation in rough set-based multicriteria analysis. The semantic models of these logics are data tables, each of which is comprised of a finite set of objects described by a finite set of criteria/attributes. The domains of the criteria may have ordinal properties expressing preference scales, while the domains of the attributes may not. The validity, support, and confidence of a rule are defined via its satisfaction in the data table.

Coordinating Interorganizational Workflows Based on Process-Views

In multi-enterprise cooperation, an enterprise must monitor the progress of private processes as well as those of the partners to streamline interorganizational workflows. In this work, a process-view model, which extends beyond the conventional activity-based process model, is applied to design workflows across multiple enterprises. A process-view is an abstraction of an implemented process. An enterprise can design various process-views for different partners according to diverse commercial relationships, and establish an integrated process that is comprised of private processes as well as the process-views that these partners provide. Participatory enterprises can obtain appropriate progress information from their own integrated processes, allowing them to collaborate more effectively. Furthermore, interorganizational workflows are coordinated through virtual states of process-views. This work develops a regulated approach to map the states between private processes and process-views. The proposed approach enhances prevalent activity-based process models to be adapted in open and collaborative environments.

Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables

In this paper, we propose a dominance-based fuzzy rough set approach for the decision analysis of a preference-ordered uncertain or possibilistic data table, which is comprised of a finite set of objects described by a finite set of criteria. The domains of the criteria may have ordinal properties that express preference scales. In the proposed approach, we first compute the degree of dominance between any two objects based on their imprecise evaluations with respect to each criterion. This results in a valued dominance relation on the universe. Then, we define the degree of adherence to the dominance principle by every pair of objects and the degree of consistency of each object. The consistency degrees of all objects are aggregated to derive the quality of the classification, which we use to define the reducts of a data table. In addition, the upward and downward unions of decision classes are fuzzy subsets of the universe. Thus, the lower and upper approximations of the decision classes based on the valued dominance relation are fuzzy rough sets. By using the lower approximations of the decision classes, we can derive two types of decision rules that can be applied to new decision cases.

Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations

Providing various e-services on the Internet by enterprises is an important trend in e-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.

Mining the change of event trends for decision support in environmental scanning

As the business environment has become increasingly complex, the demand for environmental scanning to assist company managers plan strategies and responses has grown significantly. The conventional technique for supporting environmental scanning is event detection from text documents such as news stories. Event detection methods recognize events, but neglect to discover the changes brought about by the events. In this work, we propose an event change detection (ECD) approach that combines association rule mining and change mining techniques. The approach detects changes caused by events to help managers respond rapidly to changes in the external environment. Association rule mining is used to discover event trends (the subject patterns of events) from news stories. The changes can be identified by comparing event trends in different time periods. The empirical evaluation showed that the discovered event changes can support decision-makers by providing up-to-date information about the business environment, which enables them to make appropriate decisions. The proposed approach is practical for business managers to be aware of environmental changes and adjust their business strategies accordingly.