基于矢量量化编码的协同过滤推荐方法

Collaborative Filtering Recommendation Method Based on Vector Quantization Coding

随着互联网的高速发展,海量数据涌现,使得推荐系统成为计算机科学领域的研究热点。变分自编码器已经被成功应用于协同过滤方法的设计中,并取得了出色的推荐效果。然而,以往基于变分自编码器的推荐模型存在一些问题,如对隐变量先验分布的约束以及“后验失效”等,这些问题降低了推荐模型的性能。为了解决这一问题,使变分自编码器模型更加适用于推荐任务,提出了一种基于矢量量化编码的协同过滤推荐方法。该方法采用离散的矢量编码代替变分自编码器从隐变量分布中直接取样获得编码,从观测数据中学习到一个离散的潜在表示,提高了编码的表示能力。在多个公开数据集上的性能评测结果显示,与现有方法相比,所提方法能够有效提升推荐性能。

With the rapid development of the Internet, the emergence of massive data makes recommender system become a research hotspot in the field of computer science.Variational autoencoders(VAE) have been successfully applied to the design of collaborative filtering methods and achieved excellent recommendation results. However, there are some defects in the previous VAE-based models, such as the problems of prior constraint and the “posterior collapse”,which essentially reduce their recommendation performance.To address this issue while enabling the latent variable model more suitable for the recommendation task, a novel collaborative filtering recommendation model based on latent vector quantization is proposed in this paper.By encoding the discrete vectors instead of directly sampling from the distribution of latent variables, our method can learn discrete representations that are consistent with the observed data, which greatly improves the capability of latent vector encoding and the learning ability of the model.Extensive evaluations conducted on three benchmark datasets demonstrate the effectiveness of the proposed model.Our model can significantly improve the recommendation performance compared with existing state-of-the-art methods while learning more expressive latent representations.