A contextual-bandit approach to personalized news article recommendation

Lihong Li, Wei Chu, John Langford, Robert E. Schapire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

940 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Pages661-670
Number of pages10
DOIs
StatePublished - Jul 21 2010
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: Apr 26 2010Apr 30 2010

Publication series

NameProceedings of the 19th International Conference on World Wide Web, WWW '10

Other

Other19th International World Wide Web Conference, WWW2010
CountryUnited States
CityRaleigh, NC
Period4/26/104/30/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Keywords

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

Fingerprint Dive into the research topics of 'A contextual-bandit approach to personalized news article recommendation'. Together they form a unique fingerprint.

Cite this