混合深层协同过滤的SVD++推荐方法

SVD++ model of hybrid deep collaborative filtering for recommender method

为抑制辅助信息在推荐模型中各个方向的扰动并考虑使用文本信息提取项目特征,提出一种矩阵分解模型,混合深层协同过滤的SVD++推荐方法.该模型将附加栈式降噪自编码器和堆叠的收缩降噪自编码器与辅助信息相结合,分别提取用户和项目的潜在特征表示,并在提取项目特征表示时加入预训练的词嵌入模型考虑词语之间的语义关系.在数据集MovieLens-1M与MovieLens-10M的实验.结果表明:相比于传统算法、深度学习算法以及所提模型的变体,所提模型更有效地提取潜在特征表示并提高预测评分精度.

In order to suppress the disturbance of auxiliary information in various directions in the recommendation model and consider using text information to extract item features,this paper proposes a matrix factorization model,which is a SVD++model of hybrid deep collaborative filtering for recommender method(SHD).The model combines additional stacked denoising autoencoder(ASDAE)and stacked contractive denoising autoencoder(CDAE)with auxiliary information to extract latent feather representations of users and items,and adds pre-trained word embedding model to consider the semantic relationship of words when extracting item feature representations.Experimental results on the MovieLens1 M and MovieLens10 M show that compared with traditional algorithms,deep learning algorithms,and variants of the proposed model,the proposed model is more effective in extracting latent feature representations and improving the accuracy of prediction score.