Name
Abstract | ||
|---|---|---|
We present a general treatment of the problem of aggregating preferences from several experts into a consensus ranking, in the context where information about a target ranking is available. Specifically, we describe how such problems can be converted into a standard learning-to-rank one on which existing learning solutions can be invoked. This transformation allows us to optimize the aggregating function for any target IR metric, such as Normalized Discounted Cumulative Gain, or Expected Reciprocal Rank. When applied to crowdsourcing and meta-search benchmarks, our new algorithm improves on state-of-the-art preference aggregation methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2396868 | CIKM |
Keywords | Field | DocType |
target ranking,consensus ranking,general treatment,normalized discounted cumulative gain,meta-search benchmarks,target ir,expected reciprocal rank,aggregating function,new algorithm,aggregating preference,aggregating expert preference,meta search,preference aggregation,crowdsourcing | Data mining,Learning to rank,Aggregation problem,Reciprocal,Metasearch engine,Information retrieval,Ranking,Crowdsourcing,Computer science,Normalized discounted cumulative gain,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.44 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maksims Volkovs | 1 | 216 | 14.48 |
| Hugo Larochelle | 2 | 7692 | 488.99 |
| Richard S. Zemel | 3 | 4958 | 425.68 |