Gender prediction model based on CNN-BiLSTM-attention hybrid

Accurate gender prediction is crucial for businesses to offer personalized services to their customers. To address the issue of low prediction accuracy typically associated with traditional machine learning techniques in commercial recommendation systems, a parallel hybrid prediction model was proposed. This model combines convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and an attention mechanism, forming a hybrid CNN-BiLSTM-attention mechanism model. By leveraging the CNN's ability to capture local features, the BiLSTM's strength in processing the contextual information of sequential data, and the attention mechanism's focus on relevant data, the model improves gender prediction accuracy. Additionally, the research utilized the ANOVA method and random forest models to extract relevant features, applied the continuous bag of word (CBOW) algorithm to vectorize clickstream text data, and employs the parallel CNN-BiLSTM-attention mechanism model for gender prediction. The results show that this proposed model outperforms individual models in gender prediction accuracy. This development establishes a strong foundation for merchant recommendation systems, allowing them to deliver more accurate service recommendations.