A Contextual-Bandit Approach to Personalized News Article Recommendation

TL;DR

This paper introduces a new efficient contextual bandit algorithm for personalized news recommendation, demonstrating significant click rate improvements using large-scale offline evaluation on real-world data.

Contribution

The paper presents a novel, computationally efficient contextual bandit algorithm and an offline evaluation method for personalized news recommendation systems.

Findings

Achieved a 12.5% increase in user clicks over standard algorithms.

Validated the approach on a dataset with over 33 million events.

Demonstrated greater advantages when data is limited.

Abstract

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.