The utilization of text classification techniques has demonstrated great promise in the field of detecting consumer fraud based on consumer reviews. However, persistent challenges remain in handling large samples at the borders and identifying unforeseen fraud behaviors. To address these challenges, we propose a novel approach that combines a channel biattention convolutional neural network (CNN) with a pretrained language model. Specifically, we propose a similarity computation module for implicitly learning a metric matrix to characterize the similarity between prior knowledge and consumer reviews in vector space. Through this process, the model is able to learn and understand the relationship between prior knowledge and corresponding samples during training, thereby improving its ability to identify unforeseen fraudulent behaviors. Additionally, we propose a channel biattention CNN module to adaptively emphasize the importance of relevant prior knowledge to enhance the model’s ability to accurately classify boundary samples. To ensure effective model training, we expand and organize a real-world dataset, reducing noise and increasing the number of fraud samples available for analysis. Experimental results demonstrate that our approach achieves state-of-the-art performance in fraud detection. Notably, our model is capable of detecting unforeseen fraud cases without the need for retraining or fine-tuning, making it highly adaptable and efficient in practical applications.

Traditional service composition methods usually address the constraint-satisfied service composition (CSSC) problem with static web services. Such solutions however are inapplicable to the dynamic scenarios where the services or their QoS values may change over time. Some recent studies are proposed to use reinforcement learning, especially, integrate the idea of Q-learning, to solve the dynamic CSSC problem. However, such Q-learning algorithm relies on Q-table to search for optimal candidate services. When the problem of CSSC becomes complex, the number of states in Q-table is very large and the cost of the Q-learning model will become extremely high. In this paper, the authors propose a novel solution to address this issue. By training a DQN network to replace the Q-table, this solution can effectively model the uncertainty of services with fine-grained QoS attributes and choose suitable candidate services to compose on the fly in the dynamic scenarios. Experimental results on both artificial and real datasets demonstrate the effectiveness of the method.