Communications of the ACM
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A large-scale analysis of query logs for assessing personalization opportunities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
Context ontologies for recommending from the social web
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
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The contextual recommender task is the problem of making useful offers, e.g., placing ads or related links on a web page, based on the context information, e.g., contents of the page and information about the user visiting, and information on the available alternatives, i.e., the advertisements or relevant links. In the case of ads for example, the goal is to select ads that result in high click rates, where the (ad) click rate is some unknown function of the attributes of the context and ad. We describe the task and make connections to related problems including recommender and multi-armed bandit problems.