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This paper is concerned with the problem of unsupervised rank aggregation in the context of metasearch in information retrieval. In such tasks, we are given many partial ordered lists of retrieved items provided by many search engines and we want to define a way for aggregating those lists in order to find out a consensus. One classical approach consists in aggregating, for each retrieved item, the scores given by the different search engines. Then, we use the resulting aggregated scores distribution in order to infer a consensus ordered list. In this paper we investigate whether aggregation operators defined in the fields of multi-sensor fusion and multicriteria decision making are of interest for metasearch problems or not. Moreover, another purpose of this paper is to introduce a new aggregation operator, its foundations and its properties. We finally test all these aggregation operators for metasearch tasks using the Letor 2.0 dataset. Our results show that among the studied aggregation functions, the ones which are more compensatory outperform the baseline methods CombSUM and CombMNZ.