A vector space model for automatic indexing
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Combinatorial Information Market Design
Information Systems Frontiers
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Knowledge and Data Engineering
A market-based approach to recommender systems
ACM Transactions on Information Systems (TOIS)
The Wisdom of Crowds
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Information Market-Based Decision Fusion
Management Science
Computational Statistics & Data Analysis
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Hybrid web recommender systems
The adaptive web
A new understanding of prediction markets via no-regret learning
Proceedings of the 11th ACM conference on Electronic commerce
Combining predictions for accurate recommender systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the effectiveness of collaborative recommendation with ontology-based user profiles
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Using Social Media to Predict Future Events with Agent-Based Markets
IEEE Intelligent Systems
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Recommender Systems have emerged as a way to tackle the overload of information reflected in the increasing volume of information artefacts in the web and elsewhere. Recommender Systems analyse existing information on the user activities in order to estimate future preferences. However, in real life situations, different types of information can be found and their interpretation can vary as well. Each recommender system implements a different approach for utilizing the known information and predicting the user preferences. A problem is that of blending the recommendations in an adaptive, intuitive way while performing better than base recommenders. In this work we propose an approach based on information markets for the fusion of recommender systems. Information Markets have unique characteristics that make them suitable building blocks for ensemble recommenders. We evaluate our approach with the Movielens and Netflix datasets and discuss the results of our experiments.