基于栈式降噪自编码器的协同过滤算法

Stacked denoising autoencoder for collaborative filtering algorithm

针对协同过滤推荐准确性的现状进行了研究,提出一种基于栈式降噪自编码器的协同过滤算法。栈式降噪自编码器是一种典型的深度学习网络模型,具有强大的特征提取能力。用户对项目的评分作为输入,训练网络,学习出项目的隐含特征编码,用PCA对项目属性降维并计算属性相似性,结合隐性编码计算的相似性作为最终结果,根据最终的项目相似性产生top-N推荐列表。Movie Lens数据集的实验表明,该算法能够有效提升推荐结果的召回率,一定程度上解决了评分矩阵稀疏与项目之间没有共同用户评分就不能计算相似性的问题。

This paper proposed a new method called stacked denoising autoencoder based on collaborative filtering after studying the way of improving precision of recommended algorithm. SDAE was a mul-layer deep learning model which could learn significant dependencies from input information. It used ratings given to items by users to learn the latent feature, got results of dimensionality reduction after PCA to calculate attribute similarities. Then it added the grade similarities computed with con- cealed feature as the final resuh. Finally, it emploied similarities between items to generate the top-N recommendation list. Experiments on MovieLens show that the proposed algorithm has higher recall rate and can settle the missing ratings and cold start problems in some way.