An Effective Complete-Web Recommender System
Full Text:
WWW03-Effective.pdf 
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, ICPageFinder, 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 ICPageFinder 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.
Citation
T. Zhu,
R. Greiner,
G. Haeubl.
"An Effective Complete-Web Recommender System".
Twelfth International World Wide Web Conference, Budapest, HUNGARY, May 2003.
| Keywords: |
WebIC, web, machine learning |
| Category: |
In Conference |
BibTeX
@incollection{Zhu+al:WWW03,
author = {Tingshao Zhu and Russ Greiner and Gerald Haeubl},
title = {An Effective Complete-Web Recommender System},
booktitle = {Twelfth International World Wide Web Conference},
year = 2003,
}Last Updated: August 13, 2007
Submitted by Russ Greiner 
