Research

RiVal was created as a research tool and has been used in the following scientific publications.

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List of research papers using RiVal

• Evaluating Decision-Aware Recommender Systems. Rus M. Mesas and Alejandro Bellogín. RecSys 2017
• Semantics-aware Recommender Systems exploiting Linked Open Data and graph-based features. Cataldo Musto, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro. Knowledge-Based Systems 136:1. 2017
• A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews. Cataldo Musto, Marco de Gemmis, Giovanni Semeraro, and Pasquale Lops. RecSys 2017
• Fuzzy Fingerprints for Item-Based Collaborative Filtering. A. Carvalho, P. Calado, J. P. Carvalho. IWIFSGN 2017, EUSFLAT 2017.
• A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. A. Suglia, C. Greco, C. Musto, M. de Gemmis, P. Lops, G. Semeraro. UMAP 2017
• Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks. C. Musto, C. Greco, A. Suglia, G. Semeraro. Italian Information Retrieval Workshop (2016)
• Empirical study of the behavior of several Recommender System methods on SAPO Videos. G. Oliveira. MSc. Thesis (2015)
• Replicable Evaluation of Recommender Systems. A. Said, A. Bellogín. RecSys 2015.
• Transparency-less recommender system evaluation leads to misleading results. A. Said, A. Bellogín. TinyToCS 3 (2015)
• Predicting User Engagement in Twitter with Collaborative Ranking. E. Diaz-Aviles, H. T. Lam, F. Pinelli, S. Braghin, Y. Gkoufas, M. Berlingerio, F. Calabrese. RecSysChallenge 2014.
• RiVal: A New Benchmarking Toolkit for Recommender Systems. A. Said, A. Bellogín. ERCIM News 99, 2014.
• RiVal – A Toolkit to Foster Reproducibility in Recommender System Evaluation. A. Said, A. Bellogín. RecSys 2014.
• Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks. A. Said, A. Bellogín. RecSys 2014.