Ranking plays an important role in information retrieval, aiming to sort the documents retrieved for a given query in the descending order of relevance. Recently, many approaches based on the idea of "learning to rank" have been proposed for doing ranking. Most of them consider all the documents of the training queries to build a static, query-independent ranking model. In this paper, we propose an adaptive, query- dependent framework for learning to rank based on a distributional similarity measure for gauging the similarity between queries. For each training query, one individual ranking model is learned from its associated set of documents. When a new query is consulted, the individual trained models of those training queries most similar to the new query are obtained and combined into a joint model which is then used to rank the documents retrieved for the new query. Experimental results show that our proposed approach works very well compared with other methods.
|Number of pages||18|
|Journal||International Journal of Innovative Computing, Information and Control|
|Publication status||Published - 2011 Dec 1|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Computational Theory and Mathematics