Abstract

There are a number of recommendation systems that can suggest the webpages, within a single website, that other (purportedly similar) users have visited. By contrast, our goal is a system that can recommend "information content" (IC) pages --- i.e., pages that contain information relevant to the user --- from anywhere in the web. This paper describes how we addressed this challenge, We first collected a number of annotated user sessions, whose pages each include a bit indicating whether it was IC. Our system, ICPF, then used this collection to learn the characteristics of words that appear in such IC-pages, in terms of the word's "browsing features" (e.g., did the user follow links whose anchor included this word, etc.). This paper

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