数据采集与处理2018，Vol.33Issue(1)：179-185,7.DOI:10.16337/j.1004-9037.2018.01.020
基于用户类别偏好相似度和联合矩阵分解的推荐算法
Recommendation Algorithm Based on User Category Preference Similarity and Joint Ma-trix Factorization
摘要
Abstract
Using context information to improve the accuracy of recommendation systems and enhance us-er experience is one of the hottest topics in the domain of recommend systems.However the issue of data sparse still challenges the existing context-aware recommender system.To better alleviate the data sparse problem,this paper proposes a rating prediction method,i.e.,joint matrix factorization with user category prefernce(JM F-UCP).Based on the joint matrix factorization,the method addresses the data sparse problem by combining user′s rating information and user category preference to predict the rating score with higher accuracy.The time complexity of the proposed method linearly increases with the num-ber of amount of dataset and is scalable to very large datasets.Experimental results on real world rating dataset MovieLens demonstrate that the proposed method can achieve better accuracy.关键词
推荐系统/联合矩阵分解/用户类别偏好/评分预测Key words
recommender system/joint matrix factorization/user category preference/rating prediction分类
信息技术与安全科学引用本文复制引用
何海洋,王勇,蔡国永..基于用户类别偏好相似度和联合矩阵分解的推荐算法[J].数据采集与处理,2018,33(1):179-185,7.基金项目
广西可信软件重点实验室(KX201625)资助项目 （KX201625）
广西密码学与信息安全重点实验室(GCIS201617)资助项目 （GCIS201617）
研究生创新基金(GDYCSZ201469)资助项目. （GDYCSZ201469）
