Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Eligibility Traces for Off-Policy Policy Evaluation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Just-in-time contextual advertising
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Efficient bandit algorithms for online multiclass prediction
Proceedings of the 25th international conference on Machine learning
Simulation Studies of Multi-armed Bandits with Covariates (Invited Paper)
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
A case study of behavior-driven conjoint analysis on Yahoo!: front page today module
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
Exploring compact reinforcement-learning representations with linear regression
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Hybrid systems for personalized recommendations
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Proceedings of the fourth ACM international conference on Web search and data mining
All the news that's fit for you
Communications of the ACM
Adaptive policies for selecting groupon style chunked reward ads in a stochastic knapsack framework
Proceedings of the 20th international conference on World wide web
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Click shaping to optimize multiple objectives
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to trade off between exploration and exploitation in multiclass bandit prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
When recommendation meets mobile: contextual and personalized recommendation on the go
Proceedings of the 13th international conference on Ubiquitous computing
Personalized pricing recommender system: multi-stage epsilon-greedy approach
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Personalized news recommendation: a review and an experimental investigation
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Hybrid-ε-greedy for mobile context-aware recommender system
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Optimistic Bayesian sampling in contextual-bandit problems
The Journal of Machine Learning Research
Language intent models for inferring user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Recommendation challenges in web media settings
Proceedings of the sixth ACM conference on Recommender systems
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
LogUCB: an explore-exploit algorithm for comments recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
Multi-faceted ranking of news articles using post-read actions
Proceedings of the 21st ACM international conference on Information and knowledge management
A contextual-bandit algorithm for mobile context-aware recommender system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
PENETRATE: Personalized news recommendation using ensemble hierarchical clustering
Expert Systems with Applications: An International Journal
Reusing historical interaction data for faster online learning to rank for IR
Proceedings of the sixth ACM international conference on Web search and data mining
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Proceedings of the sixth ACM international conference on Web search and data mining
Exploration / exploitation trade-off in mobile context-aware recommender systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Directing exploratory search: reinforcement learning from user interactions with keywords
Proceedings of the 2013 international conference on Intelligent user interfaces
SciNet: a system for browsing scientific literature through keyword manipulation
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Personalized News Recommendation Based on Collaborative Filtering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Content recommendation on web portals
Communications of the ACM
SmartAds: bringing contextual ads to mobile apps
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
All the news that's fit to read: a study of social annotations for news reading
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Representing documents through their readers
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified search federation system based on online user feedback
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a robust modeling of temporal interest change patterns for behavioral targeting
Proceedings of the 22nd international conference on World Wide Web
Choosing which message to publish on social networks: a contextual bandit approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Mixing bandits: a recipe for improved cold-start recommendations in a social network
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Ranked bandits in metric spaces: learning diverse rankings over large document collections
The Journal of Machine Learning Research
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
Exploratory and interactive daily deals recommendation
Proceedings of the 7th ACM conference on Recommender systems
Information graph model and application to online advertising
Proceedings of the 1st workshop on User engagement optimization
Personalized news recommendation based on implicit feedback
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
GeoRank: an efficient location-aware news feed ranking system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
LASER: a scalable response prediction platform for online advertising
Proceedings of the 7th ACM international conference on Web search and data mining
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
Counterfactual reasoning and learning systems: the example of computational advertising
The Journal of Machine Learning Research
Designing and deploying online field experiments
Proceedings of the 23rd international conference on World wide web
Personalized collaborative clustering
Proceedings of the 23rd international conference on World wide web
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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.