Data Fusion in Information Retrieval Using Consensus Aggregation Operators

In this paper, we address the problem of unsupervised rank aggregation in the context of meta-searching in information retrieval field. The first goal of this paper is to apply aggregation operators that are defined in information fusion domain to the particular issue mentioned beforehand. Triangular norms, conorms and quasi-arithmetic means, are such kind of operators. Then, the second goal of this work is to introduce a new aggregation function, its logical foundations and its combinatorial properties. Particularly, this operator allows to take into account the relationships between experts in a flexible way. Finally, we test these different aggregation operators on the LETOR dataset. The results of our experiments show that this kind of aggregation functions can lead to better results than baseline methods such as CombSUM and CombMNZ approaches.