A recommendation system algorithm based on large scale Internet Environment
with the growing scale of the Internet, the amount of data is increasing rapidly as well In order to improve the user experience, the recommendation system came into being.It recommends products to the user by analyzing the users behavior.In the recommendation system, collaborative filtering algorithm is one of the most widely used algorithms.While the traditional collaborative filtering is no longer suitable for large-scale network, where the algorithm efficiency is low, as well as, it has the extremely sparse problem.To solve those problems, we designed an improved collaborative filtering algorithm for network segmentation.The algorithm uses a segmentation rules to partition the large-scale network, and decompose the problem into sub-problems.Ultimately, it can help us to meet the purpose of optimizing the algorithm.
recommendation system collaborative filtering algorithm similarity calculation network segmentation sparseness
Xifeng Liu Zhijian Wang Feng Ye
Computer and Information College Hohai University Nanjing, China
国际会议
武汉
英文
108-112
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）