Users clustering Based on Search Behavior Analysis Using the LRFM Model (Case Study: Iran Scientific Information Database (Ganj))

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Abstract

Iran scientific information database (Ganj) which includes almost one million scientific records provides the search opportunity in dissertations, domestic scientific journals, articles, conferences, research projects, and governmental reports. A large number of researchers meet the needs of their scientific and research resources from Ganj database daily. Users’ needs and behaviors are variant and understanding it helps system administrators to use different strategies to manage the better databases and provide efficient services to users. One way to understand users’ needs is to cluster them based on their behavior and identify the features of each cluster. This study aims to cluster the users based on the analysis of their search behavior using the LRFM model. In this study, the search log data of Ganj users were collected for three months, the LRFM attributes were calculated, and then the K-means algorithm was applied to them. The optimal number of clusters was calculated based on different criteria. Based on customer value matrix the results of customer clustering users in four groups are efficient, suspicious, unreliable, and intermittent and based on customer loyalty Marcus users are categorized in loyal, potential, insecure and newcomers.

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