On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Semantics of quotient operators in fuzzy relational databases
Fuzzy Sets and Systems
Beyond min aggregation in multicriteria decision: (ordered) weighted min, discri-min, leximin
The ordered weighted averaging operators
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Extended Boolean information retrieval
Communications of the ACM
Information Retrieval: Algorithms and Heuristics
Information Retrieval: Algorithms and Heuristics
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Experiments on using fuzzy quantified sentences in adhoc retrieval
Proceedings of the 2004 ACM symposium on Applied computing
ETAK: tailoring architectural evolution by (re-)using architectural knowledge
Proceedings of the 2010 ICSE Workshop on Sharing and Reusing Architectural Knowledge
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Classical information retrieval (IR) methods use the sum for aggregating term weights. In some cases, this may diminish the discriminating power between documents because some information is lost in this aggregation. To cope with this problem, the paper presents an approach for ranking documents in IR, based on a refined vector-based ordering technique taken from multiple criteria analysis methods. Different vector representations of the retrieval status values are considered and compared. Moreover, another refinement of the sum-based evaluation that controls if a term is worth adding or not (in order to avoid noise effect) is considered. The proposal is evaluated on a benchmark collection that allows us to compare the effectiveness of the approach with respect to a classical one. The proposed method provides some improvement of the precision w.r.t Mercure IR system.