Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/10018
Título: Personalised fading for stream data
Autor: Veloso, Bruno
Malheiro, Benedita
Burguillo, Juan Carlos
Foss, Jeremy
Palavras-chave: Forgetting Technique
Stream Mining
Fading Strategies
Data: 2017
Editora: ACM Press
Relatório da Série N.º: SAC '17;
Resumo: This paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n viewer ratings. As new viewer events occur, they are inserted in the viewer queue, by shifting and fading the queue ratings, and the viewer latent model is faded. We explored time, rating-and-position and popularity-based fading techniques, using the latter as the base fading algorithm. This approach attempts to address the problem of dynamic viewer profile updating (volatile preferences) as well as the problem of bounded processing resources (fixed size queues). The results show that our approach outperforms previous approaches, improving the quality of the predictions.
Peer review: yes
URI: http://hdl.handle.net/10400.22/10018
DOI: http://dx.doi.org/10.1145/3019612.3019868
ISBN: 9781450344869
Versão do Editor: http://dl.acm.org/citation.cfm?doid=3019612.3019868
Aparece nas colecções:ISEP – LSA – Comunicações em eventos científicos

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