一种基于降噪自编码器的隐式反馈推荐算法

现有的隐式反馈协同算法直接利用稀疏的二值社交信任信息辅助推荐,存在严重的数据稀疏问题,且没有深层次地融合社交信任信息的影响。针对以上问题,提出利用降噪自编码器深度融合用户隐式反馈数据与社交信息的算法。首先从不同的角度区分用户信任,提出一种信任相似度的新度量方法来改善社交数据的稀疏性,利用降噪自编码器将信任数据与用户隐式交互信息深度融合,通过综合二者的影响,有效提高了推荐质量。实验表明,该算法优于现有主流的的隐式反馈推荐算法。

An implicit feedback recommendation algorithm based on denoising autoencoder

The existing implicit feedback collaborative algorithms directly use sparse binary social trust information to assist in recommendation, which has serious data sparseness problems. Besides, there is no impact of deep integration of social trust information. Aiming at the above problems, an algorithm is proposed, which uses the denoising autoencoder to deeply fuse the user's implicit feedback data and social information. Firstly, we distinguish the trust of users from different angles, propose a new measurement method of trust similarity, and improve the sparseness of social trust data. Secondly, denoising autoencoders are used to deeply fuse trust data and user implicit interactive information. By combining the advantages of the two, the recommendation quality is effectively improved. Experiments show that this method is superior to the existing mainstream implicit feedback recommendation algorithms.