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SCIENTIA SINICA Informationis, Volume 47 , Issue 8 : 997(2017) https://doi.org/10.1360/N112016-00306

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

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  • ReceivedDec 31, 2016
  • AcceptedMar 6, 2017
  • PublishedJun 9, 2017

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.


Funded by

国家重点基础研究发展计划(973)(2014CB340503)

国家自然科学基金(61472107,61632011)


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  • Table 1   Examples of common user expression
    No. Intentional dialogue Unintentional dialogue
    1 下周出差飞哈尔滨 你叫什么名字
    2 附近有住宿的地方吗 今天的天气如何
    3 到北京的飞机 我刚下飞机, 到北京了
    4 火车硬座票还有吗 坐了一天的火车,还是硬座
    5 和颐酒店大床房 和颐酒店事件
  • Table 2   Experimental results of intention recognition
    Model Precision Recall F-Measure
    SVM 0.9116 0.7769 0.8153
    CNN 0.9469 0.9139 0.9278
    LSTM 0.9475 0.9106 0.9258
    Convolutional-LSTM 0.9514 0.9442 0.9473

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