【摘要】 针对互联网站点信息海量和结构复杂的趋势,推荐系统被用来协助互联网用户方便快捷地找到所需信息,培养用户忠诚度。Web挖掘技术在处理海量数据和稀疏数据上有着先天的优势,所以Web挖掘技术在推荐系统中得到了越来越广泛的研究和应用。基于Web挖掘的推荐系统所使用的主要技术有聚类、关联规则、序列模式等等。然而,这些技术往往不能在推荐的准确性和覆盖范围方面做到两全。综合这几种技术,取其优点去其缺点,提出了一种新的算法(AIR算法)。通过基于实际使用数据的详尽的实验评估,可以证明该算法能够在准确性和覆盖范围方面明显提高推荐系统的整体性能。更多还原

【Abstract】 Internet users often spend much time finding useful pages.Recommendation system does such a job that it can help user locate information and increase the users’ loyalty.In respect that web mining is good at dealing with massive data and sparse data,web mining is widely used in recommendation system.Most important technologies in web mining include clustering,association rules,sequence pattern.Howerver,these technologies have a limitation that it is difficult to strike the balance of precision and coverage.In this paper,we integrate these technologies and raise a new algorithm(AIR).Our experiment data,performed on real usage data,indicate that AIR can achieve dramatic improvements in terms of recommendation effectiveness.更多还原