个性化推荐推荐系统中基于WEB的挖掘

摘要

Due to the rapid development and wide applications, Internet has led to the information explosion on the WEB. It becomes research hotspots at present how to design effective algorithms and systems based on the technology of WEB Usage Mining. In this paper, we develop a recommendation system called WRS (Web Recommendation System), which is based on the application of WEB. In WRS, we propose a novel algorithm that makes use of the technology of image segmentation to cluster access modules, and adopts the parallel longest common subsequence algorithm to discern users' behaviors. Theoretical analysis and laboratory result show that our system is more effective and the recommendation performance is improved after using the new method.%Internet的普及和应用带来了WEB上的信息爆炸,如何基于WEB挖掘技术设计有效的信息推荐算法和推荐系统成为当前的研究热点.开发了一种基于WEB使用的推荐系统WRS (Web Recommendation System),在该系统中,提出了一种利用图形分割技术聚类用户访问模式的算法,并采用最长公共子序列算法对用户目前的行为进行识别.理论分析和实验结果表明,改进后的模型在推荐质量上有了较大提高.