Personalized News Recommendation Based on Click Behavior
Venue
2010 International Conference on Intelligent User Interfaces
Publication Year
2010
Authors
Jiahui Liu, Elin Pedersen, Peter Dolan
BibTeX
Abstract
Online news reading has become very popular as the web provides access to news
articles from millions of sources around the world. A key challenge of news service
website is help users to find news articles that are interesting to read. In this
paper, we present our research on developing personalized news recommendation
system in Google News. The recommendation system builds profiles of user’s news
interests based on user’s click behavior on the website. To understand the news
interest change over time, we first conducted a large-scale log analysis of the
click behavior of Google News users. Based on the log analysis, we developed a
Bayesian framework for predict user’s current news interests, which considers both
the activities of that particular user and the news trend demonstrated in
activities of a group of users. We combine the information filtering mechanism
using learned user profile with an existing collaborative filtering mechanism to
generate personalized news recommendation. The combined method was deployed in
Google News. Experiments on the live traffic of Google News website demonstrated
that the combined method improves the quality of news recommendation and attracts
more frequent visit to the website.
