Personalized News Recommendation Based on Click Behavior
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.
Research Areas
