【摘要】 个性化推荐是电子商务和搜索引擎中最常见的应用之一，但由于推荐计算时常用的用户-项目评分矩阵非常稀疏，导致推荐效果不佳。针对评分矩阵的稀疏性问题，提出了一种双自编码器矩阵分解模型(Dual Auto Encoders Matrix Factorization, DAE-MF),该模型使用二路并行网络结构，结合卷积自编码器和栈式降噪自编码器以及矩阵分解技术，有效提高了模型的数据补全能力，在MovieLens和AIV数据集上的对比实验表明所提出的DAE-MF方法在评级预测任务中优于现有模型。更多还原

【Abstract】 Recommendation system is one of the most common and basic applications in E-Commerce, search engines, etc.However, the user-item rating matrix data in the recommendation system is extremely sparse, resulting in poor recommendation results.This paper proposes a dual auto encoders matrix factorization(DAE-MF) model for the sparseness of the scoring matrix.This model uses a two-way parallel network structure, which combines a convolutional auto encoder and stacked denoising autoencoder and matrix factorization technology, fully combining the advantages of the two modules, effectively improving the model’s data completion capabilities.Through experiments on MovieLens and AIV datasets, it is verified that the DAE-MF method is superior to the existing models in rating prediction tasks.更多还原