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The Implementation of a Personalized Reading System

Different readers are usually interested in different aspects of an article. One aspect of content may be distributed at different locations within an article. Furthermore, users often wish to obtain extended content for further understanding. Most recommendation systems recommend one or multiple pieces of texts instead of content. It is time-consuming for readers to find the required content in recommended texts or extended content from other texts. This paper designs a system that can help reader to quickly browse the content that meets users' personalized interests on one aspect or one topic. The system takes a User Interest Model as input, and retrieves the extension of the reading content from the references or other articles that quotes this article. Experimental system demonstrates that the reading system has potential for quickly reading a long article.