• Publications
  • Influence
Unifying collaborative and content-based filtering
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approachExpand
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A joint framework for collaborative and content filtering
This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework incorporates all availableExpand
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Past, Present, and Future of Recommender Systems: An Industry Perspective
When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of recommender systems in real-world applications. The competition provided many lessons, and many more have beenExpand
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Recommender Systems in Industry: A Netflix Case Study
The Netflix Prize put a spotlight on the importance and use of recommender systems in real-world applications. Many the competition provided many lessons about how to approach recommendation and manyExpand
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Artwork personalization at netflix
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not endExpand
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Copy of The Cognitive Foundry: A Flexible Platform for Intelligent Agent Modeling.
The Cognitive Foundry is a unified collection of tools for Cognitive Science and Technology applications, supporting the development of intelligent agent models. The Foundry has two primaryExpand
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DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combinedExpand
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Performance Assessment to Enhance Training Effectiveness
Training simulators have become increasingly popular tools for instructing humans on performance in complex environments. However, the question of how to provide individualized and scenario-specificExpand
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Collaborative Machine Learning
In information retrieval, feedback provided by individual users is often very sparse. Consequently, machine learning algorithms for automatically retrieving documents or recommending items may notExpand
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Using Navigation to Improve Recommendations in Real-Time
Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to aExpand
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