基于深度学习特征表示协同过滤算法

Research on Cooperative Filtering Algorithm Based on Deep Learning Feature Representation

在推荐系统中,单一的学习矩阵分解的内积交互或者利用深度神经网络来捕获用户与项目交互,不足以有效地学习用户与项目的潜在特征。针对这一问题,提出一种在显式反馈与隐式反馈基础上,称为基于深度学习特征表示的协同过滤算法(DLFeaCF)。该模型首先学习用户与项目的内积与外积交互;然后在内积的基础上,从隐式映射与特征映射两个方面再利用多层感知机(MLP)的非线性交互学习能力去获取用户与项目的全局特征;同时在外积的基础上,利用CNN学习捕获用户与项目的局部特征;最后在融合层组合特征并获得预测分数。在真实的MovieLens数据集上进行实验,表明DLFeaCF模型能获得更好的推荐性能。

In the recommendation system,the inner product interaction of a single learning matrix decomposition or the use of deep neural networks to capture user interaction with the project is not sufficient to effectively learn the potential characteristics of users and projects.In view of this problem,this paper proposes a collaborative filtering algorithm based on deep learning feature representation(DLFeaCF)based on display feedback and implicit feedback.The model first learns the inner product and outer product interaction between the user and the project.Then,based on the inner product,it uses the nonlinear interactive learning ability of the multi-layer perceptron(MLP)to obtain the two aspects from implicit mapping and feature mapping;on the basis of the outer product,CNN learning is used to capture the local features of the user and the project;finally,combine the features in the fusion layer and obtain the prediction score.The experiments on the real MovieLens dataset show that the DLFeaCF model can achieve better recommendation performance.