基于堆栈降噪自编码器改进的混合推荐算法

Improved hybrid recommendation algorithm based on stacked denoising autoencoder

针对传统协同过滤算法仅利用评分信息作为推荐依据,没有利用用户评论和标签信息,无法准确反映用户对项目特征的偏好,推荐精确度低且容易过拟合等问题,提出一种基于堆栈降噪自编码(SDAE)改进的混合推荐(SDHR)算法。首先利用深度学习模型SDAE从用户自由文本标签中抽取项目的显式特征信息;然后,改进隐因子模型(LFM)算法,使用显式项目特征信息替换LFM中的抽象特征,进行矩阵分解训练;最后通过用户-项目偏好矩阵为用户提供推荐。在公开数据集Movie Lens上的实验测试,与三组推荐模型(基于标签权重及协同过滤、基于SDAE和极限学习机、基于循环神经网络)比较,该算法推荐精确度分别提高了45.2%、38.4%和16.1%。实验结果表明,所提算法可以充分利用项目自由文本标签信息提高推荐性能。

Concerning the problem that traditional collaborative filtering algorithm just utilizes users＇ ratings on items when generating recommendation, without considering users＇ labels or comments, which can not reflect users＇ real preference on different items and the prediction accuracy is not high and easily overfits, a Stacked Denoising AutoEncoder （SDAE）-based improved Hybrid Recommendation （SDHR） algorithm was proposed. Firstly, SDAE was used to extract items＇ explicit features from users＇ free-text labels. Then, Latent Factor Model （LFM） algorithm was improved, the LFM＇s abstract item features were replaced with extracted explicit ones to train matrix decomposition model. Finally, the user-item preference matrix was used to generate recommendations. Experimental tests on the dataset MovieLens showed that the accuracy of the proposed algorithm was improved by 38.4%, 16.1% and 45.2% respectively compared to the three recommendation models （including the model based on label-based weights with collaborative filtering, the model based on SDAE and extreme learning machine, and the model based on recurrent neural networks）. The experimental results show that the proposed algorithm can make full use of items＇ free-text label information to improve recommendation performance.