基于变分自编码器的混合推荐算法

Hybrid Recommendation Algorithm Based on Variational Auto-Encoder

较多传统推荐算法因未考虑曝光因素而难以解决冷启动问题。为此,通过引入曝光隐变量,提出一种基于变分自编码器的混合推荐算法。在协同过滤背景下使用马尔科夫链蒙特卡洛采样推断曝光隐变量和特征向量,在推断过程中将前一次迭代得到的分布结果作为先验,利用共轭关系直接得到参数后验,以提高推断精度。在此基础上,通过变分自编码器VAEe抽取用户曝光向量的隐特征,据此对该用户做曝光预测,同时训练变分自编码器VAEi抽取商品的协同隐特征,解决新商品的冷启动问题。在真实数据集上的实验结果表明,该算法能够同时提高旧商品和新商品的推荐性能。

Most of the traditional recommendation algorithms fail to effectively solve the cold start problem for they do not consider the exposure factor.To this end,this paper introduces the exposure hidden variable and proposes a hybrid recommendation algorithm based on Variational Auto-Encoder(VAE).In the context of collaborative filtering,the algorithm uses Markov Chain Monte Carlo(MCMC)sampling to make the inference of the exposure hidden variable and feature vectors.In the inference process,the distribution result obtained from the previous iteration is used as a priori,and the conjugate relationship is used to directly obtain the posteriori of parameters to improve the inference accuracy.On this basis,the VAEe is used to extract the hidden features of the user’s exposure vector,so as to make the exposure prediction for the user,and the VAEi is trained to extract the collaborative hidden features of the product,so as to solve the cold start problem of the new product.Experimental results on the real world dataset show that the proposed algorithm improves the recommending performance of the old products and new products at the same time.