Ciência-IUL
Publicações
Descrição Detalhada da Publicação
Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Ano (publicação definitiva)
2021
Língua
Inglês
País
Portugal
Mais Informação
Web of Science®
Scopus
Esta publicação não está indexada na Scopus
Google Scholar
Abstract/Resumo
This work studies and compares the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform Steam, which contains information about the game usage, corresponding to the implicit feedback that was later transformed into explicit feedback. These algorithms were implemented using the Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k. We have concluded that computationally low demanding approaches can still obtain suitable results.
Agradecimentos/Acknowledgements
--
Palavras-chave
Recommendation system,Collaborative filtering,Implicit feedback
Prémios
Best Poster Award
Registos de financiamentos
Referência de financiamento | Entidade Financiadora |
---|---|
39703 | PT2020 |
UIDB/50021/2020 | Fundação para a Ciência e a Tecnologia |
Projetos Relacionados
Esta publicação é um output do(s) seguinte(s) projeto(s):