基于高斯混合变分自编码器的协同过滤

Gaussian mixture variational autoencoder for collaborative filtering

摘要:

基于变分自编码器的协同推荐算法可以帮助解决推荐算法中的稀疏性问题，但是由于变分自编码器模型先验是单一的高斯分布，使得表达趋向简单和平均，存在拟合不足的问题.高斯混合变分自编码器模型拥有更加复杂的先验，相对于原本的变分自编码器模型，它对于非线性的任务有着更强的适应性和效果，已被广泛应用于无监督聚类和半监督学习.受此启发，本文研究基于高斯混合变分自编码器模型的协同过滤算法.本文基于Cornac推荐系统比较框架设计实验，将高斯混合变分自编码器改进后用于协同推荐任务中，利用生成模型重新生成的用户物品矩阵进行推荐.在推理模型和生成模型中分别用一层隐藏层提取深层特征增加模型鲁棒性，并且使用提前停止的训练策略以减少过拟合.本文在多组公开数据集上进行实验，与其他推荐算法在NDCG和召回率指标上进行对比.实验证明，改进的基于高斯混合变分自编码器模型的协同过滤算法在推荐任务中表现优异.

Abstract:

The collaborative recommendation algorithm based on the Variational Autoencoder (VAE) can help solve the sparsity problem in the recommendation algorithm, but the VAE model’s prior is a single Gaussian distribution, which makes the expression tends to be simple and average, and suffers from the problem of underfitting. The Gaussian Mixture Variational Autoencoder (GMVAE) model has a more complex prior, which is more adaptable and effective for nonlinear tasks compared to the original VAE model, and has been widely used for unsupervised clustering and semisupervised learning. Inspired by this, this paper investigates a collaborative filtering algorithm based on the GMVAE model. In this paper, the authors design experiments based on the Cornac recommender system comparison framework, and use the improved GMVAE for the collaborative recommendation task, the useritem matrix regenerated by the generative model is used for recommendation task. Deep features are extracted with one hidden layer in the inference model and one layer in the generation model to increase model robustness, and an early stop strategy is used to reduce overfitting. In this paper, experiments are conducted on multiple public datasets to compare with other recommendation algorithms in terms of NDCG and recall metrics. The experiments demonstrate that the improved collaborative filtering algorithm based on a GMVAE model performs well in the recommendation task.