| 2021 | ICASSP | Replacing Human Audio with Synthetic Audio for on-Device Unspoken Punctuation Prediction. | Daria Soboleva, Ondrej Skopek, Mrius Sajgalk, Victor Carbune, Felix Weissenberger, Julia Proskurnia, Bogdan Prisacari, Daniel Valcarce, Justin Lu, Rohit Prabhavalkar, Balint Miklos |
| 2020 | ECAI | Shallow Neural Models for Top-N Recommendation. | Alfonso Landin, Daniel Valcarce, 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 | RecSys | On the robustness and discriminative power of information retrieval metrics for top-N recommendation. | Daniel Valcarce, Alejandro Bellogn, Javier Parapar, Pablo Castells |
| 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 |
| 2016 | ECIR | Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation. | Daniel Valcarce, Javier Parapar, Alvaro Barreiro |
| 2016 | ECIR | Language Models for Collaborative Filtering Neighbourhoods. | Daniel Valcarce, Javier Parapar, Alvaro Barreiro |
| 2015 | ECIR | A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems. | Daniel Valcarce, Javier Parapar, Alvaro Barreiro |
| 2015 | RecSys | Exploring Statistical Language Models for Recommender Systems. | Daniel Valcarce |
| 2015 | RecSys | A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. | Daniel Valcarce, Javier Parapar, Alvaro Barreiro |