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Publicação
CrowdTargeting: Making Crowds More Personal
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Orientador(es)
Resumo(s)
Crowdsourcing is a bubbling research topic that has the potential to be applied in numerous online and social scenarios. It consists on obtaining services or information by soliciting contributions from a large group of people. However, the question of defining the appropriate scope of a crowd to tackle each scenario is still open. In this work we compare two approaches to define the scope of a crowd in a classification problem, casted as a recommendation system. We propose a similarity measure to determine the closeness of a specific user to each crowd contributor and hence to define the appropriate crowd scope. We compare different levels of customization using crowd-based information, allowing non-experts classification by crowds to be tuned to substitute the user profile definition. Results on a real recommendation data set show the potential of making crowds more personal, i.e. of tuning the crowd to the crowdtarget.
Descrição
Article number 6735562
Palavras-chave
Crowdsourcing Recommendation Systems Customization Text Classification
Contexto Educativo
Citação
J. Costa, C. Silva, B. Ribeiro and M. Antunes, "CrowdTargeting: Making Crowds More Personal," 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization, Bayonne, France, 2013, pp. 21-26, doi: 10.1109/SMAP.2013.20.
Editora
IEEE Canada
Licença CC
Sem licença CC