Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
SIAM Journal on Discrete Mathematics
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
An outranking approach for rank aggregation in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An Unsupervised Learning Algorithm for Rank Aggregation
ECML '07 Proceedings of the 18th European conference on Machine Learning
On data fusion in information retrieval using different aggregation operators
Web Intelligence and Agent Systems
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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.