Biography:
Prof. Dr. Pablo Sánchez Pérez graduated in Computer Science at the Universidad Autónoma de Madrid in 2016. He obtained his PhD degree in Computer and Telecommunication Engineering in 2021 from the same university being part of the "Information Retrieval Group" research group. During his PhD years he has been researching in the areas of Information Retrieval and Recommender Systems, publishing papers in journals and scientific conferences. Also, during those years, he has been teaching computer programming (lab assignments). In addition, in 2022, he obtained the extraordinary PhD award in Computer and Telecommunication Engineering at the Universidad Autónoma de Madrid. He currently teaches at the Universidad Pontificia Comillas in the subjects of Fundamentals of Operating Systems, Programming, Databases, and Algorithms and Data Structures at the School of Engineering (ICAI), while continuing his research on the aforementioned topics.
Areas of interest:
Machine learning, recommender systems, information retrieval.
Experience:
Universidad Autónoma de Madrid Madrid, Spain.
POSDOCTORAL RESEARCHER July 2021 - August 2022
• Main task: research in recommender systems (especially in Point-Of-Interest recommendation) proposing and analyzing different models in the
Information Retrieval Group at the Universidad Autónoma de Madrid.
Universidad Autónoma de Madrid Madrid, Spain.
PREDOCTORAL RESEARCHER July 2018 - July 2021
• Main task: use of contextual dimensions such as sequentiality and time in recommender systems (mainly in neighborhood-based algorithms).
Universidad Autónoma de Madrid Madrid, Spain.
RESEARCHER INTERN IN THE INFORMATION RETRIEVAL GROUP September 2016 - July 2017
• Research and development of different recommender systems while studying the master’s degree.
Universidad Autónoma de Madrid Madrid, Spain.
WEB PROGRAMMER AND DATABASE DESIGN AND MANAGEMENT September 2015 - July 2016
• Development of the Escuela Politécnica Superior’s website and database (faculty of the Universidad Autónoma de Madrid).
Skills:
Programming languages: Java (Nivel alto), Python, Bash, C, SQL (medium level). Facility to learn any programming language).
Software development: NetBeans, Eclipse and Visual Studio (general programming) PostgreSQL, SQLServer y MySQL (databases).
Operating Systems: Microsoft Windows, GNU/Linux.
Other software: LaTeX, Microsoft Office, Libre Office.
Other skills: Responsibility, Commitment, Empathy, Motivation to learn, Organization.
Current research interests:
Machine Learning Artificial Inteligence Recommender Systems Information Retrieval
P. Sánchez, A. Bellogín, "Smart imputation, better recommendations: improving traditional Point-of-Interest recommendation through data augmentation", ACM Transactions on Intelligent Systems and Technology, Vol. 16, nº. 4, pp. 95, August 2025. [Online: June 2025] doi: 10.1145/3744347
JCR-JIF: 10,700 Q1 (2025); - SJR: 2,065 Q1 (2025); 
L.W. Dietz, P. Sánchez, A. Bellogín, "Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithms", Information Technology & Tourism, Vol. 27, nº. 1, pp. 75 - 124, March 2025. [Online: January 2025] doi: 10.1007/s40558-024-00304-0
JCR-JIF: 8,600 Q1 (2025); - SJR: 1,876 Q1 (2025); P. Sánchez, A. Bellogín, L. Boratto, "Correction to: Bias characterization, assessment, and mitigation in location-based recommender systems", Data Mining and Knowledge Discovery, Vol. 38, nº. 6, pp. 4233, November 2024. [Online: February 2024] doi: 10.1007/s10618-024-01013-2
JCR-JIF: 4,300 Q2 (2024); 5,500 Q1 (2025) - SJR: 1,019 Q1 (2024); 1,200 Q1 (2025) P. Sánchez, A. Bellogín, L. Boratto, "Bias characterization, assessment, and mitigation in location-based recommender systems", Data Mining and Knowledge Discovery, Vol. 37, pp. 1885 - 1929, September 2023. [Online: February 2023] doi: 10.1007/s10618-022-00913-5
JCR-JIF: 2,800 Q2 (2023); 5,500 Q1 (2025) - SJR: 1,813 Q1 (2023); 1,200 Q1 (2025) P. Sánchez, A. Bellogín, "Smart imputation, better recommendations: improving traditional Point-of-Interest recommendation through data augmentation", ACM Transactions on Intelligent Systems and Technology, Vol. 16, nº. 4, pp. 95, August 2025. [Online: June 2025] doi: 10.1145/3744347
JCR-JIF: 10,700 Q1 (2025); - SJR: 2,065 Q1 (2025); L.W. Dietz, P. Sánchez, A. Bellogín, "Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithms", Information Technology & Tourism, Vol. 27, nº. 1, pp. 75 - 124, March 2025. [Online: January 2025] doi: 10.1007/s40558-024-00304-0
JCR-JIF: 8,600 Q1 (2025); - SJR: 1,876 Q1 (2025); P. Sánchez, A. Bellogín, L. Boratto, "Correction to: Bias characterization, assessment, and mitigation in location-based recommender systems", Data Mining and Knowledge Discovery, Vol. 38, nº. 6, pp. 4233, November 2024. [Online: February 2024] doi: 10.1007/s10618-024-01013-2
JCR-JIF: 4,300 Q2 (2024); 5,500 Q1 (2025) - SJR: 1,019 Q1 (2024); 1,200 Q1 (2025) P. Sánchez, A. Bellogín, L. Boratto, "Bias characterization, assessment, and mitigation in location-based recommender systems", Data Mining and Knowledge Discovery, Vol. 37, pp. 1885 - 1929, September 2023. [Online: February 2023] doi: 10.1007/s10618-022-00913-5
JCR-JIF: 2,800 Q2 (2023); 5,500 Q1 (2025) - SJR: 1,813 Q1 (2023); 1,200 Q1 (2025)
JCR-JIF: 16,600 Q1 (2022); 30,400 Q1 (2025) - SJR: 4,457 Q1 (2022); 5,985 Q1 (2025) P. Sánchez, A. Bellogín, "On the effects of aggregation strategies for different groups of users in venue recommendation", Information Processing & Management, Vol. 58, nº. 5, pp. 102609, September 2021. [Online: February 2021] doi: 10.1016/j.ipm.2021.102609
JCR-JIF: 7,466 Q1 (2021); 8,100 Q1 (2025) - SJR: 1,854 Q1 (2021); 1,845 Q1 (2025)
JCR-JIF: 4,412 Q1 (2020); 4,500 Q2 (2025) - SJR: 1,092 Q1 (2020); 0,855 Q1 (2025)
JCR-JIF: 6,222 Q1 (2020); 8,100 Q1 (2025) - SJR: 1,061 Q1 (2020); 1,845 Q1 (2025)
JCR-JIF: 4,787 Q1 (2019); 8,100 Q1 (2025) - SJR: 1,192 Q1 (2019); 1,845 Q1 (2025)
JCR-JIF: 4,305 Q1 (2017); 6,000 Q1 (2025) - SJR: Q1 (2017); 1,507 Q1 (2025) 
