Browsing support by highlighting keywords based on user's browsing history

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 enables us to detect "familiar words" for the user. We extract keywords at the current page, which are relevant to the familiar words, 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. We show the effectiveness of our system by experiments.