计算机应用与软件2017，Vol.34Issue(12)：206-210,5.DOI:10.3969/j.issn.1000-386x.2017.12.039
基于隐语义模型的个性化推荐
LATENT FACTOR MODEL BASED PERSONALIZED RECOMMENDATION
范慧婷 1钟春琳 1龚海华1
作者信息
- 1. 中国科学技术大学计算机科学与技术学院,安徽合肥230027
- 折叠
摘要
Abstract
Many traditional recommendation methods,such as collaborative filtering or low rank matrix factorization,have data sparsity and cold-start problems on items or users.In order to overcome these two problems,this paper proposes a personalized recommendation method based on the latent factor model.By analyzing behaviors of users,we utilize latent factor model to infer user interest feature vector so as to make a personalized recommendation.Our two kinds of experiments on realistic movie data demonstrate the efficacy of the proposed method,as well as the superiority compared to traditional collaborative filtering methods and content-based methods.关键词
隐语义模型/稀疏性/冷启动/个性化推荐Key words
Latent factor model/Data sparsity/Cold-start/Personalized recommendation分类
信息技术与安全科学引用本文复制引用
范慧婷,钟春琳,龚海华..基于隐语义模型的个性化推荐[J].计算机应用与软件,2017,34(12):206-210,5.
