  In light of the tremendous amount of data produced by social media, a large
body of research have revisited the relevance estimation of the users'
generated content. Most of the studies have stressed the multidimensional
nature of relevance and proved the effectiveness of combining the different
criteria that it embodies. Traditional relevance estimates combination methods
are often based on linear combination schemes. However, despite being
effective, those aggregation mechanisms are not effective in real-life
applications since they heavily rely on the non-realistic independence property
of the relevance dimensions. In this paper, we propose to tackle this issue
through the design of a novel fuzzy-based document ranking model. We also
propose an automated methodology to capture the importance of relevance
dimensions, as well as information about their interaction. This model, based
on the Choquet Integral, allows to optimize the aggregated documents relevance
scores using any target information retrieval relevance metric. Experiments
within the TREC Microblog task and a social personalized information retrieval
task highlighted that our model significantly outperforms a wide range of
state-of-the-art aggregation operators, as well as a representative learning to
rank methods.
