Helpdesk databases are used to store past interactions between customers and companies to improve customer service quality. One common scenario of using helpdesk database is to find whether recommendations exist given a new problem from a customer. However, customers often provide incomplete or even inaccurate information. Manually preparing a list of clarification questions does not work for large databases. This paper investigates the problem of automatic generation of a minimal number of questions to reach an appropriate recommendation. This paper proposes a novel dynamic active probing method. Compared to other alternatives such as decision tree and case-based reasoning, this method has two distinctive features. First, it actively probe the customer to get useful information to reach the recommendation, and the information provided by customer will be immediately used by the method to dynamically generate the next questions to probe. This feature ensures that all available information from the customer is used. Second, this method is based on a probabilistic model, and uses a data augmentation method which avoids overfitting when estimating the probabilities in the model. This feature ensures that the method is robust to databases that are incomplete or contain errors. Experimental results verify the effectiveness of our approach.
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