基于Spark并行化改进混合地点推荐  

Improving Hybrid Location Recommendation System Based on Spark Parallelization

作　　者：蒲鑫 孟祥茹[2] 高岑[2] 王美吉[2] 刘锦扬 PU Xin;MENG Xiang-Ru;GAO Cen;WANG Mei-Ji;LIU Jin-Yang(School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;School of Statistics,Chengdu University of Information Technology,Chengdu 610103,China)

机构地区：[1]中国科学院大学计算机科学与技术学院,北京100049 [2]中国科学院沈阳计算技术研究所,沈阳110168 [3]成都信息工程大学统计学院,成都610103

出　　处：《计算机系统应用》2019年第10期86-91,共6页Computer Systems & Applications

摘　　要：推荐算法是数据挖掘中最重要的算法之一.地点推荐是推荐系统的重要研究内容.针对目前地点推荐面临的数据稀疏、冷启动、个性化程度低等问题,设计并实现了基于Spark并行化处理的改进混合地点推荐模型.该算法融合了基于内容的推荐和基于协同过滤的推荐,结合了用户当前的偏好和其他用户的意见.使用基于用户-地点属性偏好的矩阵填充方式,以此改善数据稀疏性问题;同时,对于海量数据,系统采用Spark分布式集群实现并行计算,缩短了模型训练时间.实验结果表明,与其他推荐算法相比,该算法能有效改善数据稀疏性、提升推荐效果.The recommendation algorithm is one of the most important algorithms in data mining. Location recommendation is an important research content of the recommendation system. Aiming at the problems of sparse data, cold start and low degree of personalization, the improved hybrid location recommendation algorithm based on Spark parallelization is designed and implemented. The algorithm combines content-based recommendations and collaborative filtering-based recommendations, combines the user’s current preferences with the opinions of other users. We improve data sparsity by using a matrix fill based on user preferences for location attributes;Also, for massive data, the system uses Spark distributed cluster to realize parallel computing, which shortens the model training time. Experimental results show that compared with other recommended algorithms, the proposed algorithm can effectively improve data sparsity and improve recommendation.

关 键 词：地点推荐 混合模型 数据填充 协同过滤 SPARK 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]