基于半自动编码器的协同过滤推荐算法

为高效利用推荐系统中用户和物品的交互历史和辅助信息，提出一种改进的协同过滤推荐算法。利用半自动编码器对用户和物品的辅助信息进行特征提取，将提取出的特征映射到矩阵分解模型中，通过反向传播算法实现半自动编码器与矩阵分解模型的联合更新以提升推荐效果。在MovieLens-100K和Book-Crossing公开数据集上的实验结果表明，与融合偏置的奇异值分解、概率矩阵分解等传统推荐算法相比，该算法具有更低的均方根误差和更好的推荐性能。

Collaborative Filtering Recommendation Algorithm Based on Semi-Autoencoder

To effectively use the user-item interaction history and auxiliary information in recommendation systems,this paper proposes an improved collaborative filtering recommendation algorithm.Based on semi-autoencoder,the features of auxiliary information of users and items are extracted,and then mapped into the Matrix Factorization(MF)model.By using the back propagation algorithm,the semi-autoencoder and the matrix factorization model are jointly updated to improve the recommendation performance.Experimental results on the public datasets of MovieLens-100K and Book-Crossing show that the proposed algorithm provides better recommendation effects than the traditional recommendation algorithms,including the Biased Singular Value Decomposition(Biased SVD)and the Probabilistic Matrix Factorization(PMF)algorithm.