Browsing support by highlighting keywords based on a user's browsing history
Yutaka Matsuo, Hayato Fukuta, Mitsuru Ishizuka · 2003
We develop a browsing support system which learns user's interests and highlights keywords based on a user's browsing history. Monitoring the user's access to the Web enable us to detect "familiar words" for the user. We extract keywords, which are relevant to the familiar words in the current page, and highlight them. The relevancy is measured by the biases of co-occurrence, called IRM (Interest Relevance Measure). Our system consists of three components; a proxy server which monitors access to the Web, a frequency server which stores frequency of words in the accessed Web pages, and a keyword extraction module. Preliminary reports are shown to evaluate the system.