A User Purchase Trend Analysis Model Based on Self-Supervised Learning and CNN-LSTM Architecture

With the development of e-commerce, the precision of accurately predicting user purchasing trends has been of great significance for personalized recommendation and marketing. However, the traditional purchasing behavior prediction methods are faced with the challenges of sparse data, long-term dependence modeling difficulties, and difficulty in effectively capturing the complex purchasing patterns and long-term behavior trends of users. In this paper, a user purchase trend analysis model combining self-supervised learning and convolutional neural network-long short-term memory architecture is proposed to solve the shortcomings of traditional methods in processing time series data and unlabeled data. Firstly, self-supervised learning is used to pre-train a large number of unlabeled user behavior data and extract potential features. The convolutional neural network-long short-term memory architecture is then used to combine local features and long-term dependencies to accurately predict user purchasing trends.