Predicting the Situational Relevance of Health Web Documents

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Oroszlanyova, M (Autor) Outra; Carla Teixeira Lopes (Autor) FEUP; Sérgio Nunes (Autor) FEUP; Cristina Ribeiro (Autor) FEUP

Ata de Conferência Internacional

Título: 2017 12TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

12th Iberian Conference on Information Systems and Technologies (CISTI)

Lisbon, PORTUGAL, JUN 21-24, 2017

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00N-9T5

DOI: 10.23919/cisti.2017.7975854

Abstract (EN): Relevance is usually estimated by search engines using document content, disregarding the user behind the search and the characteristics of the task. In this work, we look at relevance as framed in a situational context, calling it situational relevance, and analyze if it is possible to predict it using documents, users and tasks characteristics. Using an existing dataset composed of health web documents, relevance judgments for information needs, user and task characteristics, we build a multivariate prediction model for situational relevance. Our model has an accuracy of 77.17%. Our findings provide insights into features that could improve the estimation of relevance by search engines, helping to conciliate the systemic and situational views of relevance. In a near future we will work on the automatic assessment of document, user and task characteristics.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6