ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于图和改进K近邻模型的高效协同过滤推荐算法

1. 1(北京交通大学计算机与信息技术学院 北京 100044);2(北京交通大学信息中心 北京 100044);3(南京师范大学计算机科学与技术学院 南京 210023) (huanyum@bjtu.edu.cn)
• 出版日期: 2017-07-01
• 基金资助:
国家重点研发计划项目(2016YFB1200100)；国家自然科学基金项目(61202429,61572256)；中央高校基本科研业务费专项资金项目(2015JBM042)；江苏省自然科学基金项目(BK20141454)

### An Efficient Collaborative Filtering Algorithm Based on Graph Model and Improved KNN

Meng Huanyu1, Liu Zhen1, Wang Fang2, Xu Jiadong1, Zhang Guoqiang3

1. 1(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044);2(Information Technology Center, Beijing Jiaotong University, Beijing 100044);3(School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023)
• Online: 2017-07-01

Abstract: With the rapid development of Internet, recommender system has been considered as a typical method to deal with the over-loading of Internet information. The recommender system can partially alleviate user’s difficulty on information filtering and discover valuable information for the active user. Collaborative filtering algorithm has the advantages of domain independence and supports users’ potential interests. For these reasons, collaborative filtering has been widely used. Because the user item rating matrix is sparse and in large-scale, recommender system is facing big challenges of precision and performance. This paper puts forward a GK-CF algorithm. By building a graph-based rating data model, the traditional collaborative filtering, graph algorithms and improved KNN algorithm have been integrated. Through the message propagation in the graph and the improved user similarity calculation model, candidate similar users will be selected firstly before the calculation of users similarity. Based on the topology of bipartite graph, the GK-CF algorithm ensures the quick and precise location of the candidate items through the shortest path algorithm. Under the parallel graph framework, GK-CF algorithm has been parallelized design and implement. The experiments on real world clusters show that: compared with the traditional collaborative filtering algorithm, the GK-CF algorithm can better improve recommendation precision and the rating accuracy. The GK-CF algorithm also has good scalability and real-time performance.