J
Jesús Bobadilla
Researcher at Technical University of Madrid
Publications - 71
Citations - 5500
Jesús Bobadilla is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 23, co-authored 65 publications receiving 4565 citations. Previous affiliations of Jesús Bobadilla include Instituto Politécnico Nacional & Polytechnic University of Puerto Rico.
Papers
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Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Journal ArticleDOI
A collaborative filtering approach to mitigate the new user cold start problem
TL;DR: A new similarity measure perfected using optimization based on neural learning is presented, which exceeds the best results obtained with current metrics and achieves important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations.
Journal ArticleDOI
A new collaborative filtering metric that improves the behavior of recommender systems
TL;DR: A new metric is presented which combines the numerical information of the votes with independent information from those values, based on the proportions of the common and uncommon votes between each pair of users, which is superior to the traditional levels of accuracy.
Journal ArticleDOI
Collaborative filtering adapted to recommender systems of e-learning
TL;DR: To achieve this objective, some new equations are designed in the nucleus of the memory-based collaborative filtering, in such a way that the existent equations are extended to collect and process the information relative to the scores obtained by each user in a variable number of level tests.
Journal ArticleDOI
A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model
TL;DR: A novel technique for predicting the tastes of users in recommender systems based on collaborative filtering is presented, based on factorizing the rating matrix into two non negative matrices whose components lie within the range 0, 1 with an understandable probabilistic meaning.