A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems. - Daniel Valcarce; Javier Parapar; Alvaro Barreiro (ECIR, 2015)

Exploring Statistical Language Models for Recommender Systems. - Daniel Valcarce (RecSys, 2015)

A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. - Daniel Valcarce; Javier Parapar; Alvaro Barreiro (RecSys, 2015)

A Distributed Recommendation Platform for Big Data. - Daniel Valcarce; Javier Parapar; Alvaro Barreiro (J. Univers. Comput. Sci., 2015)

Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (CERI, 2016)

Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation. - Daniel Valcarce; Javier Parapar; Alvaro Barreiro (ECIR, 2016)

Language Models for Collaborative Filtering Neighbourhoods. - Daniel Valcarce; Javier Parapar; Alvaro Barreiro (ECIR, 2016)

Computing Neighbourhoods with Language Models in a Collaborative Filtering Scenario. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (IIR, 2016)

Item-based relevance modelling of recommendations for getting rid of long tail products. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (Knowl. Based Syst., 2016)

Combining Top-N Recommenders with Metasearch Algorithms. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (SIGIR, 2017)

Axiomatic Analysis of Language Modelling of Recommender Systems. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2017)

Term Association Measures for Memory-based Recommender Systems. - Eva Suárez-García; Alfonso Landin; Daniel Valcarce; Álvaro Barreiro (CERI, 2018)

Query Expansion as a Matrix Factorization Problem: Extended Abstract. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (CERI, 2018)

On the robustness and discriminative power of information retrieval metrics for top-N recommendation. - Daniel Valcarce; Alejandro Bellogín; Javier Parapar; Pablo Castells (RecSys, 2018)

LiMe: linear methods for pseudo-relevance feedback. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (SAC, 2018)

A MapReduce implementation of posterior probability clustering and relevance models for recommendation. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (Eng. Appl. Artif. Intell., 2018)

Finding and analysing good neighbourhoods to improve collaborative filtering. - Daniel Valcarce; Javier Parapar; Álvaro Barreiro (Knowl. Based Syst., 2018)

Item-driven group formation. - Daniel Valcarce; Igo Ramalho Brilhante; José A. F. de Macêdo; Franco Maria Nardini; Raffaele Perego 001; Chiara Renso (Online Soc. Networks Media, 2018)

PRIN: A Probabilistic Recommender with Item Priors and Neural Models. - Alfonso Landin; Daniel Valcarce; Javier Parapar; Álvaro Barreiro (ECIR (1), 2019)

Collaborative filtering embeddings for memory-based recommender systems. - Daniel Valcarce; Alfonso Landin; Javier Parapar; Álvaro Barreiro (Eng. Appl. Artif. Intell., 2019)

Information retrieval models for recommender systems. - Daniel Valcarce (SIGIR Forum, 2019)

Shallow Neural Models for Top-N Recommendation. - Alfonso Landin; Daniel Valcarce; Javier Parapar; Álvaro Barreiro (ECAI, 2020)

Assessing ranking metrics in top-N recommendation. - Daniel Valcarce; Alejandro Bellogín; Javier Parapar; Pablo Castells (Inf. Retr. J., 2020)

The Coruña corpus tool: Ten years on. - Anabella Barsaglini-Castro; Daniel Valcarce (Proces. del Leng. Natural, 2020)

Replacing Human Audio with Synthetic Audio for on-Device Unspoken Punctuation Prediction. - Daria Soboleva; Ondrej Skopek; Márius Sajgalík; Victor Carbune; Felix Weissenberger; Julia Proskurnia; Bogdan Prisacari; Daniel Valcarce; Justin Lu; Rohit Prabhavalkar; Balint Miklos (ICASSP, 2021)