面向异构单类协同过滤的阶段式变分自编码器

Staged variational autoencoder for heterogeneous one-class collaborative filtering

在推荐系统领域,大部分现有的工作主要关注仅有一种类型的用户反馈(如购买反馈)的单类协同过滤(OCCF)问题。然而,在现实的应用中,用户的反馈往往是异构的,因此如何对用户的异构反馈进行建模从而准确刻画用户的真实偏好成为了一个新的挑战。围绕异构单类协同过滤(HOCCF)问题(包含了用户的购买反馈和浏览反馈),提出了一个迁移学习解决方案——阶段式变分自编码器(SVAE)模型。首先,将用户的浏览反馈当作辅助数据,以多项式变分自编码器(Multi-VAE)为基础模型学习并生成隐特征向量;然后迁移该隐特征向量到另一路Multi-VAE,用于帮助该Multi-VAE对用户的目标数据(即购买反馈)进行建模。三个真实数据集上的实验结果显示,在多数情况下,SVAE模型在精确度(Precision@5)、归一化折损累计增益(NDCG@5)等重要指标上的表现显著优于其他流行的推荐算法,验证了所提模型的有效性。

In recommender system field,most of the existing works mainly focus on the One-Class Collaborative Filtering(OCCF)problem with only one type of users’feedback,e.g.,purchasing feedback.However,users’feedback is usually heterogeneous in real applications,so it has become a new challenge to model the users’heterogeneous feedback to capture their true preferences.Focusing on the Heterogeneous One-Class Collaborative Filtering(HOCCF)problem(including users’purchasing feedback and browsing feedback),a transfer learning solution named Staged Variational AutoEncoder(SVAE)model was proposed.Firstly,the latent feature vectors were generated via the Multinomial Variational AutoEncoder(Multi-VAE)with users’browsing feedback auxiliary data.Then,the obtained latent feature vectors were transferred to another Multi-VAE to assist the modeling of users’target data,i.e.,purchasing feedback by this Multi-VAE.Experimental results on three real-world datasets show that the performance of SVAE model on the important metrics such as Precision@5 and Normalized Discounted Cumulative Gain@5(NDCG@5)is significantly better than the performance of the state-of-the-art recommendation algorithms in most cases,demonstrating the effectiveness of the proposed model.