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pro vyhledávání: '"Vinagre João"'
News recommender systems are hindered by the brief lifespan of articles, as they undergo rapid relevance decay. Recent studies have demonstrated the potential of content-based neural techniques in tackling this problem. However, these models often in
Externí odkaz:
http://arxiv.org/abs/2411.08700
Autor:
Picoito Célia, Caria Helena, Angelino Fernando, Dourado Alcina, Pereira Nuno, Serralha Fátima, Ruivo Alice, Vinagre João, Ramos Bernardo, Pires José
Publikováno v:
SHS Web of Conferences, Vol 160, p 01001 (2023)
Nowadays we witness numerous partnerships and projects of national and international scope around active training, using digital tools and based on co-creation processes between different partners, to obtain more creative and appropriate processes to
Externí odkaz:
https://doaj.org/article/723ba7225578458c800baf6d41cbc6cb
The amount and dissemination rate of media content accessible online is nowadays overwhelming. Recommender Systems filter this information into manageable streams or feeds, adapted to our personal needs or preferences. It is of utter importance that
Externí odkaz:
http://arxiv.org/abs/2309.03512
Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness")
Externí odkaz:
http://arxiv.org/abs/2305.09319
Sharing of telecommunication network data, for example, even at high aggregation levels, is nowadays highly restricted due to privacy legislation and regulations and other important ethical concerns. It leads to scattering data across institutions, r
Externí odkaz:
http://arxiv.org/abs/2205.07829
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 feedback - e.g., ratings, views, reads, clicks -, together with context da
Externí odkaz:
http://arxiv.org/abs/2201.05156
A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are observed in uneven time intervals the time series is referred to as ir
Externí odkaz:
http://arxiv.org/abs/2112.14806
Ranking evaluation metrics are a fundamental element of design and improvement efforts in information retrieval. We observe that most popular metrics disregard information portrayed in the scores used to derive rankings, when available. This may pose
Externí odkaz:
http://arxiv.org/abs/1612.06136
Akademický článek
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Publikováno v:
In Information Fusion December 2021 76:75-86