基于混合神经网络的协同过滤推荐模型

数据稀疏性是推荐系统中严重影响推荐结果准确性的重要因素之一.针对数据稀疏性提出了融合卷积神经网络（convolutional neural network,CNN）和降噪自编码（denoisingauto-encoder,DAE）神经网络混合的神经网络评分预测模型（convolutional-denosing autoencoder,CDAE）对用户未评分项目进行预测评分，从而解决数据稀疏性问题.首先将向量化后的用户评论数据通过卷积神经网络训练得到用户特征向量矩阵，其次将用户特征向量矩阵作为降噪自编码神经网络的初始权重，结合用户评分数据经过降噪自编码神经网络训练，得到用户-项目预测评分，然后在此基础上进行基于用户的协同过滤推荐.最后使用movielens-1M实验数据集对比验证了提出的混合神经网络协同过滤推荐（convolutional-denosing autoencoder collaborative filtering,CDAECF）模型.实验证明，所提出的CDAECF模型能够有效地结合隐性反馈和显性反馈数据，具有较高的推荐准确率.

Collaborative Filtering Recommendation Model Based on Hybrid Neural Network

In the recommendation system, data sparsity is one of the important factors that seriously affect the accuracy of recommendation results. Aiming at the data sparsity, this paper proposes a hybrid neural network collaborative filtering score prediction model convolutional-denosing auto-encoder (CDAE) to perform prediction scoring for solving the problem of data sparsity. The CDAE model combines a convolutional neural network (CNN) and a denoising auto-encoder (DAE) neural network. Firstly, the vectorized user review data is trained by the CNN to obtain a user feature vector matrix. Secondly, the user feature vector matrix is used as the initial weight of the DAE neural network, and the user-item prediction score is obtained by training the auto-encoder neural network in combination with user rating data. Accordingly, user-based collaborative filtering recommendations can be made. In the paper, the proposed convolutional-denosing auto-encoder collaborative filtering (CDAECF) model is experimentally verified by comparing with the experimental data set of movielens-1M. Experiment results prove that the CDAECF model can effectively combine implicit and explicit feedback data, and performs a higher recommendation accuracy rate.