An Embeddings Based Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity. - Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Julio Herce-Zelaya; Carlos Porcel; Enrique Herrera-Viedma (SoMeT, 2018)

Using Information Extracted from Microblogs in Order to Palliate the Cold Start Problem in Recommender Systems. - Julio Herce-Zelaya; Carlos Porcel; Álvaro Tejeda-Lorente; Juan Bernabé-Moreno; Enrique Herrera-Viedma (SoMeT, 2018)

Adapting Recommender Systems to the New Data Privacy Regulations. - Álvaro Tejeda-Lorente; Juan Bernabé-Moreno; Julio Herce-Zelaya; Carlos Porcel; Enrique Herrera-Viedma (SoMeT, 2018)

An automatic skills standardization method based on subject expert knowledge extraction and semantic matching. - Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Julio Herce-Zelaya; Carlos Porcel; Enrique Herrera-Viedma (ITQM, 2019)

Web platform for learning distributed databases' queries processing. - Julio Herce-Zelaya; Carlos Porcel; Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Enrique Herrera-Viedma (ITQM, 2019)

A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds. - Álvaro Tejeda-Lorente; Juan Bernabé-Moreno; Julio Herce-Zelaya; Carlos Porcel; Enrique Herrera-Viedma (ITQM, 2019)

Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology. - Carlos Porcel; Julio Herce-Zelaya; Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Enrique Herrera-Viedma (Int. J. Comput. Commun. Control, 2020)

New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. - Julio Herce-Zelaya; Carlos Porcel; Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Enrique Herrera-Viedma (Inf. Sci., 2020)

A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary. - Juan Bernabé-Moreno; Álvaro Tejeda-Lorente; Julio Herce-Zelaya; Carlos Porcel; Enrique Herrera-Viedma (Knowl. Based Syst., 2020)

Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems. - Julio Herce-Zelaya; Carlos Porcel; Álvaro Tejeda-Lorente; Juan Bernabé-Moreno; Enrique Herrera-Viedma (Inf., 2023)