摘要： 协同过滤和概率模型是位置推荐中的常用方法,但前者没有考虑用户的移动模式,后者也难以用于稀疏数据集。针对上述问题,面向稀疏数据构建基于高斯混合模型的位置推荐框架GMMSD。按时间段划分用户签到的历史数据,通过数据预处理获取用户-区域矩阵,并利用矩阵分解算法提高稀疏数据的推荐准确度,学习高斯混合模型以预测用户出现在不同区域的概率分布,从而进行位置推荐。在真实数据集上的实验结果表明,GMMSD可以有效提高稀疏数据中位置推荐的准确度。

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

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 in sparse data compared with the traditional methods.

Key words: location recommendation, Matrix Factorization (MF), Gaussian Mixture Model(GMM), mobility pattern, probability distribution

中图分类号: 

• TP391