2010 AContextualBanditApproachtoPers

• (Li et al., 2010) ⇒ Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. (2010). “A Contextual-bandit Approach to Personalized News Article Recommendation.” In: Proceedings of the 19th International Conference on World wide web. doi:10.1145/1772690.1772758

Subject Headings: Contextual Bandit Task, Contextual Bandit Algorithm, Reinforcement Learning, Item Recommendations, News Article Recommendation.

Cited By

• http://scholar.google.com/scholar?q=%22A+contextual-bandit+approach+to+personalized+news+article+recommendation%22+2010
• http://dl.acm.org/citation.cfm?id=1772690.1772758&preflayout=flat#citedby

Quotes

Author Keywords

• contextual bandit; exploration/exploitation dilemma; personalization; recommender systems; web service

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AContextualBanditApproachtoPers	Robert E. Schapire John Langford Wei Chu Lihong Li			A Contextual-bandit Approach to Personalized News Article Recommendation				10.1145/1772690.1772758		2010