Optimizing Click-Through Rate Prediction: A Model Utilizing Multi-Attention Fusion Hashing Algorithms

Click-through rate prediction plays a crucial role in contemporary recommender systems, which aim to refine recommended items by predicting the click-through rate to enhance the overall recommendation effectiveness. However, existing click-through rate prediction models still do not fully leverage both long-term and short-term user behavior information. Consequently, this paper proposes a novel click-through rate prediction model, named Interest Evolutionary Network with Hash and Multiple Attention Network (IEHAN). The model categorizes user behaviors into long-term and short-term categories, employing a hash sampling method to model long-term user behaviors. By directly filtering behavioral items with the same hash signature as the target item from the entire sequence, the IEHAN model effectively models long-term user interests, thereby reducing information loss. Additionally, the model employs the multi-head attention mechanism and the GRU model with an attention update gate to integrate the relevance of the user's interest state and the target item, thereby enhancing the impact of relevant interests on interest evolution and shaping short-term user interest. Experimental results demonstrate that the IEHAN model achieves higher AUC and lower LOSS evaluation metrics on the Taobao dataset and the Books subset of the Amazon dataset, surpassing other existing models and exhibiting superior prediction accuracy.