2014/09/23 by Bilel Moulahi, Moulahi, Bilel, Lynda Tamine +3 · 1 citation
Computer Science · #Computer science #Data Management and Algorithms #FOS: Computer and information sciences #H.1.2 #H.3.3 #H.4.0 #Information Retrieval (cs.IR) #Information Retrieval and Search Behavior #Information retrieval #Political science #Relevance (law) #Topic Modeling #cs.IR
paper · pdf · doi:10.48550/arxiv.1409.6512
published in arXiv (Cornell University) (Cornell University) · 9 pages
openalex publication_date 2014/09/23 · arxiv created 2014/09/28 · arxiv updated 2014/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.