It’s important for mobile operators to recommend new services. Traditional method is sending advertising messages to all mobile users. But most of users who are not interested in these services treat the messages as Spam. This paper presents a method to find potential customers who are likely to accept the services. This method searchs the maximum frequent itemsets which indicate potential customers’ features from a large data set of users’ information, then find potential customers from those maximum frequent itemsets by using a bayesian network classifier. Experimental results demonstrate this method can select users with higher accuracy.
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机译:An Innovative Workflow for Appropriate Selection of Subsurface-Surface Model Integration Scheme Based on Petroleum Production System Nature, User Needs,and Integrated Simulation Performance
机译:LTE通讯系统中针对同层干扰环境对微小型基地台功率控制与用户位置推荐演算法 =Femtocell Power Control and User Location Recommendation Algorithm for Co-Tier Interference Environment in LTE Communication System
机译:Computer-readable medium, communication terminal, and method for making appropriate selection between promptly receiving communication signal and reducing power consumption
机译:Computer-readable medium, communication terminal, and method for making appropriate selection between promptly receiving communication signal and reducing power consumption