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An Online Recommender System for Large Web Sites

机译:大型网站的在线推荐系统

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In this paper we propose a WUM recommender system, called SUGGEST 3.0, that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Differently from the recommender systems proposed so far, SUGGEST 3.0 does not make use of any off-line component, and is able to manage Web sites made up of pages dynamically generated. To this purpose SUGGEST 3.0 incrementally builds and maintains historical information by means of an incremental graph partitioning algorithm, requiring no off-line component. The main innovation proposed here is a novel strategy that can be used to manage large Web sites. Experiments, conducted in order to evaluate SUGGEST 3.0 performance, demonstrated that our system is able to anticipate users' requests that will be made farther in the future, introducing a limited overhead on the Web server activity.
机译:在本文中,我们提出了一个名为SUGGEST 3.0的WUM推荐器系统,该系统可动态生成指向用户尚未访问过的页面的链接,并且可能引起他的潜在兴趣。与到目前为止建议的推荐系统不同,SUGGEST 3.0不使用任何脱机组件,并且能够管理由动态生成的页面组成的网站。为此,SUGGEST 3.0通过增量图分区算法来增量构建和维护历史信息,而无需离线组件。这里提出的主要创新是可以用于管理大型网站的新颖策略。为了评估SUGGEST 3.0的性能而进行的实验表明,我们的系统能够预测用户的要求,而将来的要求会越来越高,这在Web服务器活动方面造成了有限的开销。

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