In this paper, we present our online summarization system of web topics. The user defines the topic by a set of keywords. Then the system searches the Web for the relevant documen...
This paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. The aim of the technique is to separate the init...
Razvan Stefan Bot, Yi-fang Brook Wu, Xin Chen, Qua...
PageRank is known to be an efficient metric for computing general document importance in the Web. While commonly used as a one-size-fits-all measure, the ability to produce topica...
Users prefer to navigate subjects from organized topics in an abundance resources than to list pages retrieved from search engines. We propose a framework to cluster frequent items...
In this paper, we propose an information retrieval model called Latent Interest Semantic Map (LISM), which features retrieval composed of both Collaborative Filtering(CF) and Prob...