Comparative Convolutional Dynamic Multi-Attention Recommendation Model

Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology perspective. However, most current studies based on it only focus on part of user interests; they have not mined user preferences thoroughly. To address the above problem, we propose a novel recommendation model: comparative convolutional dynamic multi-attention (CCDMA). This model provides a more accurate approach to represent user and item features and uses multi-attention-based convolutional neural networks to extract user and item latent feature vectors dynamically. The multi-attention mechanism considers both self-attention and cross-attention. Self-attention refers to the internal attention within users and items; cross-attention is the mutual attention between users and items. Moreover, we propose an optimized comparative learning framework that can mine the ternary relationships between one user and a pair of items, focusing on their relative relationship and the internal link between a pair of items. Extensive experiments on several real-world data sets show that the CCDMA model significantly outperforms state-of-the-art baselines in terms of different evaluation metrics.

Automated summary

Recent studies have shown that attention mechanisms are crucial for accurately capturing user interests in recommender systems. The proposed CCDMA model achieves higher accuracy in extracting user and item latent feature vectors, considering both self-attention and cross-attention, and optimizing a comparative learning framework, leading to significant improvements in various evaluation metrics.