计算机工程2016，Vol.42Issue(12)：171-175,5.DOI:10.3969/j.issn.1000-3428.2016.12.030
融合用户属性的隐语义模型推荐算法
Recommendation Algorithm of Latent Factor Model Fused with User Attribute
摘要
Abstract
To solve the problems of data sparsity and cold start on latent factor model recommendation algorithm,a recommendation algorithm fused with user attribute information is presented.After adding binary user attributes to the latent factor model,it finds similar users according to the attributes of the target user and importance degrees of other user attributes measured by classification model.Combining with the target user's rating information,the recommendation result is obtained.The proposed method is tested by the Movielens data set,and the result shows that it outperforms collaborative filtering algorithm and latent factor model algorithm on accuracy of recommendation results even when data is extremely sparse.关键词
推荐系统/隐语义模型/用户属性/稀疏数据/逻辑回归Key words
recommendation system/latent factor model/user attribute/sparse data/logistic regression分类
信息技术与安全科学引用本文复制引用
巫可,战荫伟,李鹰..融合用户属性的隐语义模型推荐算法[J].计算机工程,2016,42(12):171-175,5.基金项目
广东省科技厅研发与产业化项目(2013B090500038,2014B040401012). （2013B090500038,2014B040401012）
