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:2019,28(9):162-167
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基于聚类变分自编码器的协同过滤算法
(上海理工大学 管理学院, 上海 200093)
Clustering Variational Autoencoder for Collaborative Filtering
(Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)
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投稿时间:2019-02-26    修订日期:2019-03-22
中文摘要: 针对协同过滤推荐模型的数据稀疏性问题,提出一种带有聚类隐变量的变分自编码器,用于处理用户的隐式反馈数据.该深度生成模型既能学习到隐变量的特征分布,同时又能完成对特征的聚类.先以多项式似然来重构原始数据,再用贝叶斯变分推断估计参数,并且将正则化系数引入到模型当中,通过调节其大小能够避免过度正则化,使模型的拟合效果更好.这种非线性的概率模型对缺失评分的预测有更好的建模能力.在MovieLens的三个数据集上的实验结果表明,该算法相比较于其他先进的基线有更优秀的推荐性能.
Abstract:Aiming at the data sparsity problem of collaborative filtering model, a variational autoencoder with clustering latent variable is proposed to process the implicit feedback data. The deep generative model can not only learn the feature distribution of latent variable, but also complete the clustering of features. The original data is reconstructed by multinomial likelihood, the parameters are estimated by Bayesian inference, and the regularization parameter is introduced into the model. By adjusting its size, it can avoid excessive regularization and make the model fit better. A nonlinear probability model has a better ability to model the prediction of missing scores. Experimental results on three data sets of MovieLens show that the proposed algorithm has better recommended performance than the other advanced baselines.
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基金项目:国家自然科学基金(71840003);上海理工大学科技发展项目(2018KJFZ043)
引用文本:
韩浩先,叶春明.基于聚类变分自编码器的协同过滤算法.计算机系统应用,2019,28(9):162-167
HAN Hao-Xian,YE Chun-Ming.Clustering Variational Autoencoder for Collaborative Filtering.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):162-167

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