基于降噪自动编码器的推荐算法

摘要

The traditional recommendation algorithm generally uses the user project score matrix to learn the potential factors,un-derstand the user's personal preferences and make recommendations,but in practice,the score matrix is usually very sparse.Ai-ming at the shortcomings of traditional recommendation algorithm, a recommendation model based on noise reduction automatic encoder is proposed.First,two automatic encoders are used to train the potential factor matrix of the user and the project, and then the implied feature vector is input into a neural network to carry out the scoring prediction.Finally,the recommendation is made according to the new score matrix.The experimental results show that the proposed algorithm improves the recall rate of the recommended results and reduces the reconstruction error.%传统的推荐算法一般采用用户项目评分矩阵学习潜在因素,了解用户的个人偏好并作出推荐,但在实际应用中,评分矩阵通常非常稀疏.针对传统推荐算法的不足,提出一种基于降噪自动编码器的推荐模型.首先用2个自动编码器来训练用户和项目的潜在因子矩阵,然后将学习到的隐含特征向量输入一神经网络来进行评分预测,最后根据新的评分矩阵作出推荐.实验结果表明,该算法提高了推荐结果的召回率,同时缩小了重构误差.