Personalized news recommendation based on click behavior

TLDR

Online news reading has become very popular, yet news websites struggle to help users discover articles that interest them. This study aims to develop a personalized news recommendation system for Google News. The system builds user profiles from logged click behavior, uses a Bayesian framework to predict current interests from individual and aggregate trends, and combines content‑based and collaborative filtering techniques into a hybrid recommender deployed in Google News. Live‑traffic experiments show the hybrid method improves recommendation quality and increases site traffic.

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 websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.