In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spec...
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Kons...
Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system conf...
Abstract. Many e-commerce sites use a recommendation system to filter the specific information that a user wants out of an overload of information. Currently, the usefulness of the...
Learning-enhanced relevance feedback is one of the most promising and active research directions in recent year's content-based image retrieval. However, the existing approac...
Following a recently introduced perceptual model for balanced multiwavelets, we outline, in this paper, an extension of our previous work and propose a new perceptual model for sca...