Name
Abstract | ||
|---|---|---|
Research in Information Retrieval usually shows performanceimprovement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.). In this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking. In this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another. We show that the proposed method deals well with the Information Retrieval distinctive features. Experimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1277741.1277843 | SIGIR |
Keywords | Field | DocType |
rank aggregation problem,multiple method,information retrieval,data fusion problem,better rank,information retrieval distinctive feature,aggregation mechanism,suggested method,proposed method deal,outranking approach,rank aggregation method,metasearch engine,decision rule,data fusion | Decision rule,Data mining,Aggregation problem,Metasearch engine,Multiple criteria,Ranking,Information retrieval,Computer science,Sensor fusion,Artificial intelligence,Operator (computer programming),Machine learning | Conference |
Citations | PageRank | References |
60 | 1.94 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Farah | 1 | 60 | 1.94 |
| Daniel Vanderpooten | 2 | 1153 | 74.66 |