Abstract: 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 recommendation. For example, cross-domain recommendation requires the model with the ability of transfer learning, and reinforcement learning recommendation requires long-term reward modeling.

Key words: recommendation system, deep learning, neural network, attention mechanism

摘要： 探讨神经网络如何结合注意力机制及其变种，以更好地学习用户和物品间复杂和隐含的关系，从而提高推荐的准确性和个性化水平。从多层感知机、卷积神经网络、循环神经网络、自编码器、图神经网络以及反向传播神经网络这六类典型神经网络出发，研究它们与注意力机制相结合进行推荐的过程，具体结合点击率预测、标签推荐和评论评分预测等典型应用场景进行优缺点分析。通过将神经网络与注意力机制相结合，模型能够聚焦于输入数据中的关键信息，降低对次要信息的注意程度，甚至直接过滤掉无关信息。现有将注意力机制与神经网络结合的推荐模型，在很大程度上能够满足常见的推荐任务需求。但是这类模型在跨域推荐、深度强化学习推荐以及多模态推荐等复杂推荐场景中，仍面临一些挑战，例如跨域推荐需要模型具备迁移学习的能力，强化学习推荐需要进行长期奖励建模。

关键词: 推荐系统, 深度学习, 神经网络, 注意力机制