控制理论与应用2017，Vol.34Issue(6)：753-760,8.DOI:10.7641/CTA.2017.60520
结合灰色预测的动态概率矩阵分解
Dynamic probabilistic matrix factorization with grey forecast
摘要
Abstract
The goal of recommender system is to find out the items which meet the users' preferences. However users' preferences and items' features change over time that can affect the accuracy of recommender system. Many recommender systems simply employ probabilistic matrix factorization (PMF) model without addressing this issue. Motivated by the grey system theory, in this paper, the dynamics of both users and items are modeled by utilizing the grey forecast (GF) model. Accordingly, a new dynamic recommender system based on probabilistic matrix factorization and grey forecast model (DPMF-GF) is developed. Firstly, the probabilistic matrix factorization (PMF) model is used to produce user's and item's latent vectors between consecutive time windows. Next, the grey forecast model is used to predict user's and item's latent vectors in the following timestamp. The experimental results show that our model can effectively model users' dynamics and items' dynamics, and outperforms the existing state-of-the-art recommendation algorithms.关键词
推荐系统/概率矩阵分解/灰色预测模型Key words
recommender system/probabilistic matrix factorization/grey forecast model分类
信息技术与安全科学引用本文复制引用
宛袁玉,王昌栋,赵知临,赖剑煌..结合灰色预测的动态概率矩阵分解[J].控制理论与应用,2017,34(6):753-760,8.基金项目
Supported by National Natural Science Foundation (61502543, 61573387), Guangzhou Program (201508010032), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China (2014A030310180) and Fundamental Research Funds for Central Universities (16lgzd15). （61502543, 61573387）
