基于卷积神经网络交互的用户属性偏好建模的推荐模型

潜在因子模型（LFM）以其优异的性能在推荐领域得到了广泛应用。在LFM中除了使用交互数据以外，辅助信息也被引入用于解决数据稀疏的问题，从而提升推荐的性能。然而，大多数LFM仍然存在一些问题：第一，LFM在对用户进行建模时，忽略了用户如何根据其特征偏好对项目作出决策；第二，采用内积的特征交互假设特征维度之间是相互独立的，而没有考虑到特征维度之间的关联。针对上述问题，提出一种新的推荐模型：基于卷积神经网络（CNN）交互的用户属性偏好建模的推荐模型（UAMC）。该模型首先获得用户的一般偏好、用户属性和项目嵌入，然后将用户属性和项目嵌入进行交互，以探索用户不同的属性对不同项目的偏好；接着将交互过的用户偏好属性送入CNN层来探索不同偏好属性的不同维度的关联，从而得到用户的属性偏好向量；接着使用注意力机制结合用户的一般偏好和CNN层得到的属性偏好，从而获得用户的向量表示；最后采用点积来计算用户对项目的评分。在Movielens-100K、Movielens-1M和Book-crossing这三个真实的数据集上进行了实验。实验结果表明，所提模型在均方根误差（RMSE）上与稀疏数据预测的神经网络分解机（NFM）模型相比分别降低了1.75%、2.78%和0.25%，验证了在LFM的评分预测推荐中，UAMC在提升推荐精度上的有效性。

Recommendation model for user attribute preference modeling based on convolutional neural network interaction

Latent Factor Model （LFM） have been widely used in recommendation field due to their excellent performance. In addition to interactive data， auxiliary information is also introduced to solve the problem of data sparsity， thereby improving the performance of recommendations. However， most LFMs still have some problems. First， when modeling users by LFM， how users make decisions on items based on their feature preferences is ignored. Second， the feature interaction using inner product assumes that the feature dimensions are independent to each other， without considering the correlation between the feature dimensions. In order to solve the above problems， a recommendation model for User Attribute preference Modeling based on Convolutional Neural Network （CNN） interaction （UAMC） was proposed. In this model， the general preferences of users， user attributes and item embeddings were firstly obtained， and then the user attributes and item embeddings were interacted to explore the preferences of different attributes of users to different items. After that， the interacted user preference attributes were sent to the CNN layer to explore the correlation between different dimensions of different preference attributes and thus obtain the users’ attribute preference vectors. Next， the attention mechanism was used to combine the general preferences of the users with the attribute preferences obtained from CNN layer to obtain the vector representations of the users. Finally， the dot product was used to calculate the users’ ratings of the items. Experiments were conducted on three real datasets： Movielens-100K， Movielens-1M and Book-crossing. The results show that the proposed algorithm decreases the Root Mean Square Error （RMSE） by 1.75%， 2.78% and 0.25% respectively compared with the model of Neural Factorization Machine for sparse predictive analytics （NFM）， which verifies the effectiveness of UAMC model in improving the accuracy of recommendation in the rating prediction recommendation of LFM.