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With the development of internet, web information increases fast, how to filter information which users wanted quickly and accurately is becoming a big problem. But the traditional keyword based search system's recall rate and precision are yet to be improved. Kam-so, the user interesting collaborative filtering model
In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We
in this study. The first is a baseline approach which is based on simple keyword mapping technique. The second approach, Co-Citation Selection (CCS), is based on the collaborative filtering in which neighboring papers is selected and weighted into publication citation prediction. To compare between two approaches, we
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.