摘要
最近的一些研究提出将Web使用日志的挖掘技术应用于Web个人化系统中,用于克服传统个人化技术(如CF技术、基于内容的过滤技术)中存在的问题,如处理大数据量的能力较差,依赖于用户主观的登记信息,产生的用户描述是静态的,不能获取对象之间丰富的语义联系等。但是基于Web使用日志挖掘的个人化技术不能适用于用户的使用信息获取困难或者站点内容经常变化的情况。更有效的办法是将站点的内容特征和使用特征结合到一个Web挖掘结构中去,以备推荐引擎统一使用。提出了一个基于关联规则挖掘的个人化系统,它使用概念格作为存储频繁页面集的数据结构,并介绍了如何利用概念格实时地为当前活动用户产生推荐集。
Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problem associated with more traditional Web personalization techniques such as collaborative or content - based filtering. These problems include lack of scalability , reliance on subjective user ratings or static profiles,and the inability to capture a richer set of semantic relation.ships among objects. Yet, usage- based personalization can be problematic when little usage data is available pertaining to some objects or when the site content changes regularly. For more effective persunalization, both usage and content attributes of a site must he integrated into a Web mining framework and used by the recommendation engine in a uniform manner. In this paper present an association - rule- based recommendation system,which extract usage patterns from Web log file and use the concept lattice as its data structure storing frequent itemsets. And show how to use concept lattice generated to compute a recommendation set for the active user on real time.
出处
《计算机技术与发展》
2008年第2期139-142,158,共5页
Computer Technology and Development
