首页|Studies from Polytechnic University of Madrid Update Current Data on Artificial Intelligence (Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems)
Studies from Polytechnic University of Madrid Update Current Data on Artificial Intelligence (Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems)
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Investigators publish new report on Machine Learning Artificial Intelligence. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, “Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets.” Funders for this research include Spanish Government, European Union (EU), Comunidad de Madrid under Convenio Plurianual, Universidad Politecnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario. The news reporters obtained a quote from the research from the Polytechnic University of Madrid, “Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values.”
MadridSpainEuropeArtificial IntelligenceMachine LearningPolytechnic University of Madrid
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