基于图神经网络特征交叉的协同过滤算法

学习用户和项目有效的向量表示是推荐系统的核心目标,现有的推荐模型大多通过深度神经网络或专门设计的特征交叉,来学习用户-项目间的特征交叉生成用户(项目)向量表示,但并未将用户(项目)特征间的交叉信息编码到嵌入向量中充分利用特征交叉信息,且多个特征交叉信息对于生成最终的用户(项目)向量表示的影响不同.基于此,构建两个图神经网络模块,学习用户(项目)特征间的交叉信息、用户-项目之间的特征交叉信息,并通过计算注意力分数对特征交叉信息进行加权,得到用户(项目)的特征信息;然后通过门控循环神经网络(GRU)聚合原始的特征信息和网络层学习到的特征交叉信息,得到最终的用户(项目)向量表达;最后通过用户向量与项目向量的元素积得到最终的推荐结果.在数据集MovieLens 1M、Book-Crossing和Taobao上验证了模型的有效性.

Collaborative filtering algorithm based on graph neural network cross-feature

Learning effective vector representations of users and items are the core goal of the recommendation system. Most of the existing recommendation models use deep neural networks or specially designed feature crossing to learn the feature crossing between users and projects to generate user (item) vector representations, but the cross information between user (item) features is not coded into the embedding vector to make full use of the feature cross information, and multiple feature cross information has different effects on the generation of the final user (item) vector representation. Based on this, two graph neural network modules are constructed to learn the intersection information between user (item) features and the feature intersection information between user and item, following the feature cross information weighted by calculating the attention score to obtain the user (item) feature information. Then the original feature information and the feature cross information learned from the network layer are aggregated through the gated recurrent neural network (GRU) to obtain the final user (item) vector expression. Finally, the final recommendation result is obtained through the element product of user vector and item vector. The validity of the model is verified on the datasets MovieLens 1M, Book-Crossing, and Taobao.