Identification method of user's travel consumption intention in chatting robot

Abstract

Travel consumption intention in chatting robot is the users in order to meet their travel needs, express the willingness to purchase a product or service. Identifying the user's intent to consume the product can be recommended to enhance the user's experience. Traditional consumer intention recognition methods are mainly based on template matching or artificial feature sets, which are time consuming, laborious, and hard to extend. In this paper, we regard the travel consumption intention recognition task as a classification problem and combine the deep learning method to identify the intention. The proposed method does not need to construct the feature set or match templates manually. Specifically, this study uses the convolutional long short-term memory neural network (LSTM) model to identify the travel consumption intention. First, the feature extraction is carried out by creating a convolution neural network (CNN) of the user's chat text, which is then followed by a combination of features. Then, the features are sent to the LSTM to study the characteristics of the feature representation. Finally, the classification results are outputted. Experimental results show that the convolutional-LSTM model is better than the best baseline method by two percentage points on the F-measure.