30 citations · 31 across the 6 of their papers we have counts for
6 papers
Behind Recommender Systems: the Geography of the ACM RecSys Community
Lorenzo Porcaro, João Vinagre, Pedro Frau +2
The amount and dissemination rate of media content accessible online is nowadays overwhelming. Recommender Systems filter this information into manageable streams or feeds, adapted…
Federated Anomaly Detection over Distributed Data Streams
Paula Raissa Silva, João Vinagre, João Gama
Sharing of telecommunication network data, for example, even at high aggregation levels, is nowadays highly restricted due to privacy legislation and regulations and other importan…
Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021
João Vinagre, Alípio Mário Jorge, Marie Al-Ghossein +1
Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content - e.g., posts, news, products, comments -, but also user feed…
Data-Driven Relevance Judgments for Ranking Evaluation
Nuno Moniz, Luís Torgo, João Vinagre
Ranking evaluation metrics are a fundamental element of design and improvement efforts in information retrieval. We observe that most popular metrics disregard information portraye…
Improving incremental recommenders with online bagging
João Vinagre, Alípio Mário Jorge, João Gama
Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch al…
Evaluation of recommender systems in streaming environments
João Vinagre, Alípio Mário Jorge, João Gama
Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where da…