A 2020 perspective on “Scalable modelling and recommendation using wiki-based crowdsourced repositories:” Fairness, scalability, and real-time recommendation

• Autor(a) principal: Leal, Fátima
• Data de Publicação: 2020
• Outros Autores: Veloso, Bruno, Malheiro, Benedita, González-Vélez, Horacio, Carlos Burguillo, Juan
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/15481
• topic: Algorithmic Fairness; Crowdsourcing; Data Stream Mining; Profiling; Recommendation; Scalability

Wiki-based crowdsourced data sources generally lack reliability, as their provenance is not intrinsically marshalled. By using recommendation, one may arguably assess the reliability of wiki-based repositories in order to identify the most interesting articles for a given domain. In this commentary, we explore current trends in scalable modelling and recommendation methods based on side information such as the quality and popularity of wiki articles. The systematic parallelization of such profiling and recommendation algorithms allows the concurrent processing of distributed crowdsourced Wikidata repositories. These algorithms, which perform incremental updating, need further research to improve the performance and generate up-to-date high-quality recommendations. This article builds upon our previous work (Leal et al., 2019) by extending the literature review and identifying important trends and challenges pertaining to crowdsourcing platforms, particularly those of Wikidata provenance.