|NOM DE FICHIER||Learning to Rank for Information Retrieval.pdf|
|TAILLE DU FICHIER||7,22 MB|
|DATE DE PUBLICATION||2011-May-01|
Learning To Rank uses supervised machine learning to train a model not for the usual single-item classification or prediction, but to discover the best order for a list of items, using features extracted from each item to give it a ranking. It's not looking at the precise score for each item but the relative order - whether one item is ranks above or below another.
In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research ...