摘要
推荐模型通常使用用户的历史行为来获得用户偏好表示,以产生推荐。大多数方法学习到的用户表示会把不同的偏好因素纠缠在一起,而解耦学习的方法可以用于分解用户的行为特征。为此,文中提出了一个基于变分自编码器的框架DSVAECF,用于从用户历史行为中分解静态和动态偏好因素。首先,DSVAECF模型的两个编码器分别使用多层感知机和循环神经网络对用户行为进行历史行为建模,以此得到用户的静态和动态偏好表示;然后,将拼接的静态和动态偏好表示视为用户偏好的解耦表示,并将其输入解码器来捕获用户的决策,并重构出用户行为。在模型训练阶段,一方面最大化重构的用户行为与真实用户行为之间的互信息来学习模型参数;另一方面通过最小化解耦表示与其先验分布间的差异来保留模型的生成能力。在Amazon和MovieLens两个数据集上的实验结果表明,与基准方法相比,DSVAECF在归一化折损累计增益、精确率和召回率上都有显著的提升,拥有更好的推荐性能。
Recommendation models typically use user’s historical behaviors to obtain user preference representations for recommendations.Most of the methods of learning user representations always entangle different preference factors,while the disentangled learning method can be used to decompose user behavior characteristics.In this paper,a variational autoencoder based framework DSVAECF is proposed to disentangle the static and dynamic factors from user’s historical behaviors.Firstly,two encoders of the model use multi-layer perceptron and recurrent neural network to model the user behavior history respectively,so as to obtain the static and dynamic preference representation of the user.Then,the concatenate static and dynamic preference representations are treated as disentangled representation input decoders to capture user’s decisions and reconstruct user’s behavior.On the one hand,in the model training phase,DSVAECF learns model parameters by maximizes the mutual information between reconstructed user’s behaviors and actual user’s behaviors.On the other hand,DSVAECF minimizes the difference between disentangled representations and their prior distribution to retain the generation ability of the model.Experimental results on Amazon and MovieLens data sets show that,compared with the baselines,DSVAECF significantly improves the normalized discounted cumulative gain,recall,and precision,and has better recommendation performance.
作者
伍美霖
黄佳进
秦进
WU Mei-lin;HUANG Jia-jin;QIN Jin(School of Computer Science and Technology,Guizhou University,Guiyang 550025,China;International WIC Institute,Beijing University of Technology,Beijing 100000,China)
出处
《计算机科学》
CSCD
北大核心
2022年第12期163-169,共7页
Computer Science
基金
贵州省科学技术基金,黔科合基础([2020]1Y275)。
关键词
变分自编码器
深度学习
序列建模
解耦学习
协同过滤
Variational autoencoder
Deep learning
Sequence modeling
Disentangled learning
Collaborative filtering
作者简介
通讯作者:黄佳进(jhuang@bjut.edu.cn);伍美霖,gs.mlwu19@gzu.edu.cn。
