We developed a word-weighting algorithm based on the information access history of a user. The information access history of a user is represented as a set of words, and is considered to be a user model. We weight words in a document according to their relevancy to the user model. The relevancy is measured by the biases of co-occurrence, called IRM (Interest Relevance Measure), between a word in a document and words in the user model. We evaluate IRM through a constructed browsing support system, which monitors word occurrences on the user’s browsed Web pages and highlights keywords in the current page. Our system consists of three components: a proxy server that monitors access to the Web, a frequency server that stores the frequencies of words appearing on the accessed Web pages, and a keyword extraction module.