An Effective Complete-Web Recommender System

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 describes the ICPF system, as well as a tool (AIE) we developed to help users annotate their sessions, and a study we performed to collect these annotated sessions. We also present empirical data that validate the effectiveness of this approach.