| Summary: | 碩士 === 國立政治大學 === 資訊科學學系 === 100 === In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.
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