摘要
推荐系统数据库的评分数据稀少,对电影推荐的质量有所限制。为解决这个问题,提出一种同时将用户和电影元数据纳入改进的隐语义模型的模型。构造用户元数据-分类矩阵与电影元数据-分类矩阵,将分类域与隐因子空间进行映射,以此获取新用户与新电影的隐因子,进行推荐。实验结果表明,这种模型在提高预测准确率的同时,有效地解决了冷启动问题。
The rating data of the recommended system database is scarce, and the quality of the movie recommendation is limited. To solve this problem, a model that simultaneously incorporates user and movie metadata into an improved implicit semantic model is proposed. The user metadata-classification matrix and movie metadata-classification matrix are constructed, and the classification domain and the implicit factor space are mapped to obtain the hidden factors of the new user and the new movie, and a recommendation is made. The experimental results show that this model can effectively solve the cold start problem while improving the accuracy of prediction.
作者
刘春霞
陆建波
武玲梅
LIU Chun-xia;LU Jian-bo;WU Ling-mei(College of Computer ＆ Information Engineering,Guangxi Teachers Education University,Nanning 530299,China)
出处
《计算机与现代化》
2018年第11期83-87,共5页
Computer and Modernization
基金
国家自然科学基金资助项目(61672177)
关键词
推荐系统
冷启动
元数据
隐语义模型
电影推荐
recommender system
cold start
metadata
latent semantic model
movie recommendation
