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.

在本文中，我们提出了一种检测异常使用模式的高级方法，以识别用户需求的细粒度级别。以前的大多数研究都根据用户的文字评论来调查用户需求识别。因此，他们只关注产品级别的显式需求，而不关注细粒度级别的隐含需求。尽管在最近的一些研究中，作者试图基于异常检测来识别用户需求，但他们仅考虑了使用序列中的有限元素，例如触摸按钮，而没有考虑重要元素，例如拖动交互和弹出按钮。和通知组件。因此，在这项研究中，我们考虑了使用顺序的所有要素，以识别异常的使用顺序，以便在细粒度的级别上识别用户的需求。此外，我们利用递归神经网络语义变异自动编码器（RNN-SVAE）架构（一种用于句子嵌入的最新架构）来有效表示用法序列。详细地，我们利用基于异构嵌入的RNN-SVAE体系结构来计算整个使用序列的矢量表示，以应用异常检测方法来确定与用户需求相对应的异常序列。实验结果验证了我们提出的方法提取了以前的方法无法识别的有意义的异常使用模式。另外，我们提出的方法显示出异常分数和提取序列的重要性分数之间的系数分数比以前的方法具有更高的相关性。