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Introduction to recommender systems: Algorithms and Evaluation
Joseph A. Konstan
Evaluating collaborative filtering recommender systems
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets...
Ontological user profiling in recommender systems
Stuart E. Middleton, Nigel R. Shadbolt, David C. De Roure
We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from...
Latent semantic models for collaborative filtering
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed...
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
Zan Huang, Hsinchun Chen, Daniel Zeng
Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based...
Item-based top-N recommendation algorithms
Mukund Deshpande, George Karypis
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a...