Monitoring RSS Feeds based on User Browsing Pattern Abstract

RSS has been widely used to disseminate information on the Web over the years. With the help of RSS feed readers, a user may subscribe to the feeds that are published by her favorite blogs, news channels, or Websites, and access the most recent content from these information sources. However, when the size of the subscription list grows over time, it becomes less manageable for the user to catch up with the most up-to-date information. In this paper, we propose a Personal Information Manager that helps a user monitor the pool of information sources in her subscription list and recommends relevant articles based on her browsing history. In particular, in order for the manager to provide the most up-to-date content, we propose a retrieval scheduling algorithm that allocates limited system resources in an optimal way based on the user’s previous access pattern. Experiments show that our scheduling algorithm significantly improves the freshness of content when compared to other scheduling algorithms which do not take into account a user’s behavior. 1