基于降噪自编码器的社会化推荐算法

现有的社会化推荐算法未考虑信任用户对目标用户深层的偏好影响。针对这一问题,提出了一种基于深度学习的混合推荐算法,利用降噪自编码器学习用户及其信任用户的评分偏好,使用加权隐藏层来平衡这些表示的重要性,有效建模用户间的潜在偏好交互。在此基础上,通过用户聚类和个性化权重区分不同类的用户受其信任用户的影响程度。在开放数据集上的实验结果表明,该算法优于现有的社会化推荐算法,与主要的推荐算法SoRec、RSTE、SocialMF、TrustMF相比,其平均绝对误差(MAE)和均方根误差(RMSE)显著降低,获得了较好的推荐效果。

A social recommendation algorithm based on denoising auto-encoder

The existing social recommendation algorithms do not consider the influence of trusted users on the deep preference of target users. Aiming at this problem, a hybrid recommendation model based on deep learning is proposed, which uses the denoising auto-encoder to learn the rating preference of users and their trusted users, uses the weighted hidden layer to balance the importance of these representations for users, and effectively model potential interactions between users. On this basis, user clu- stering and personalization weights are used to distinguish the impact of different types of users on their trusted users. The experimental results on the public dataset show that the proposed method is superior to the existing social recommendation algorithms. Compared with the main recommendation models SoRec, RSTE, SocialMF, and TrustMF algorithm, the proposed algorithm significantly reduces the mean absolute error (MAE) and the root mean square error (RMSE), thus obtaining better recommendation effect effectively.