计算机工程2018，Vol.44Issue(1)：62-68,7.DOI:10.3969/j.issn.1000-3428.2018.01.010
稀疏数据中基于高斯混合模型的位置推荐框架
Location Recommendation Framework Based on Gaussian Mixture Model in Sparse Data
刘攀登 1刘清明2
作者信息
- 1. 四川大学计算机科学与技术学院,成都610000
- 2. 南京晓庄学院新闻传播学院,南京210017
- 折叠
摘要
Abstract
Collaborative filtering and probability model are commonly methods for location recommendation.However,the former does not consider mobility patterns of users and the latter performs poorly in sparse dataset.For lack of existing methods,this paper constructs a framework based on Gaussian Mixture Model(GMM) in sparse data,named GMMSD.The check-in history data is divided by time slot,then the user-region matrix is obtained by data preprocessing and the accuracy of recommendation is improved by Matrix Factorization (MF) algorithm in sparse data.Finally,the GMM is learned to predict the probability distribution of different locations where each user checks in.Experiments are carried out on real data sets.The results show that GMMSD can effectively improve the accuracy of location recommendation insparse data compared with the traditional methods.关键词
位置推荐/矩阵分解/高斯混合模型/移动模式/概率分布Key words
location recommendation/Matrix Factorization (MF)/Gaussian Mixture Model(GMM)/mobility pattern/probability distribution分类
信息技术与安全科学引用本文复制引用
刘攀登,刘清明..稀疏数据中基于高斯混合模型的位置推荐框架[J].计算机工程,2018,44(1):62-68,7.
