Abnormal Usage Sequence Detection for Identification of User Needs via Recurrent Neural Network Semantic Variational Autoencoder

In this paper, we propose an advanced method to detect abnormal usage patterns for identifying the fine-grained levels of user needs. Most previous studies investigated user need identification based on users textual reviews. Thus, they focused only on the explicit needs of the product levels, and not on the implied needs of the fine-grained levels. Although in a few recent studies the authors attempted to identify user needs based on abnormality detection, they considered only limited elements of the usage sequence, such as touching buttons, and did not consider the important elements, such as the dragging interaction and the pop-up and notification components. Thus, in this study, we considered all the elements of the usage sequence to identify abnormal usage sequences for recognizing user needs at the fine-grained level. Moreover, we utilized the recurrent neural network semantic variational autoencoder (RNN-SVAE) architecture, which is a state-of-the-art architecture for sentence embedding, to represent the usage sequences effectively. In detail, we calculate the vector representation of the entire usage sequence utilizing the RNN-SVAE architecture based on heterogeneous embedding to apply the abnormality detection method for determining abnormal sequences corresponding to user needs. The experimental results verify that our proposed method extracts meaningful abnormal usage patterns that previous approaches do not identify. Additionally, our proposed method shows a higher correlation of the coefficient score between the abnormality score and the importance score of the extracted sequences than do previous approaches.