| 2025 | ECIR | Towards Reliable Testing for Multiple Information Retrieval System Comparisons. | David Otero, Javier Parapar, lvaro Barreiro |
| 2025 | SIGIR | Limitations of Automatic Relevance Assessments with Large Language Models for Fair and Reliable Retrieval Evaluation. | David Otero, Javier Parapar, lvaro Barreiro |
| 2023 | ECIR | PsyProf: A Platform for Assisted Screening of Depression in Social Media. | Anxo Prez, Paloma Piot-Perez-Abadin, Javier Parapar, lvaro Barreiro |
| 2023 | SIGIR | BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression. | Anxo Prez, Javier Parapar, lvaro Barreiro, Silvia Lopez-Larrosa |
| 2021 | SAC | The wisdom of the rankers: a cost-effective method for building pooled test collections without participant systems. | David Otero, Javier Parapar, lvaro Barreiro |
| 2021 | SAC | Testing the tests: simulation of rankings to compare statistical significance tests in information retrieval evaluation. | Javier Parapar, David E. Losada, lvaro Barreiro |
| 2020 | ECAI | Shallow Neural Models for Top-N Recommendation. | Alfonso Landin, Daniel Valcarce, Javier Parapar, lvaro Barreiro |
| 2020 | ECIR | Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings. | Alfonso Landin, Javier Parapar, lvaro Barreiro |
| 2019 | ECIR | PRIN: A Probabilistic Recommender with Item Priors and Neural Models. | Alfonso Landin, Daniel Valcarce, Javier Parapar, lvaro Barreiro |
| 2018 | SAC | LiMe: linear methods for pseudo-relevance feedback. | Daniel Valcarce, Javier Parapar, lvaro Barreiro |
| 2017 | SIGIR | Combining Top-N Recommenders with Metasearch Algorithms. | Daniel Valcarce, Javier Parapar, lvaro Barreiro |