Learning for Ranking Aggregation
H Li - Learning to Rank for Information Retrieval and Natural …, 2011 - Springer
Learning to Rank for Information Retrieval and Natural Language Processing, 2011•Springer
This chapter gives a general introduction to learning for ranking aggregation. Ranking
aggregation is aimed at combining multiple rankings into a single ranking, which is better
than any of the original rankings in terms of an evaluation measure. Learning for ranking
aggregation is about building a ranking model for ranking aggregation using machine
learning techniques. Hereafter, we take meta-search as an example to make the
explanation. Without loss of generality, the technologies described can be applied to other …
aggregation is aimed at combining multiple rankings into a single ranking, which is better
than any of the original rankings in terms of an evaluation measure. Learning for ranking
aggregation is about building a ranking model for ranking aggregation using machine
learning techniques. Hereafter, we take meta-search as an example to make the
explanation. Without loss of generality, the technologies described can be applied to other …
This chapter gives a general introduction to learning for ranking aggregation. Ranking aggregation is aimed at combining multiple rankings into a single ranking, which is better than any of the original rankings in terms of an evaluation measure. Learning for ranking aggregation is about building a ranking model for ranking aggregation using machine learning techniques. Hereafter, we take meta-search as an example to make the explanation. Without loss of generality, the technologies described can be applied to other applications.
Springer