推荐系统中神经网络结合注意力机制研究综述OA北大核心CSTPCD

Explore how neural networks combine attention mechanisms and their variants to better learn complex and implicit relationships between users and items,thereby improving the accuracy and personalization of recommendations.Starting from six typical types of neural networks:multi-layer perceptron,convolutional neural network,recurrent neural network,autoencoder,graph neural network,and backpropagation neural network,this paper studies the process of combining them with the attention mechanism for recommendation.Specifically,the advantages and disadvantages are analyzed based on typical application scenarios such as click-through rate prediction,tag recommendation,and review rating prediction.By combining neural networks with attention mechanisms,the model can focus on key information in the input data,reduce attention to secondary information,and even directly filter out irrelevant information.Existing recommendation models that combine attention mechanisms with neural networks,to a large extent,can meet the needs of common recommendation tasks.However,this type of model still faces some challenges in complex recommendation scenarios such as cross-domain recommendation,deep reinforcement learning recommendation,and multi-modal recom-mendation.For example,cross-domain recommendation requires the model with the ability of transfer learning,and rein-forcement learning recommendation requires long-term reward modeling.