One of the key issues in learning to rank is document representation.In most of the learning to rank algorithms documents and queries are represented as a bag of words

and words are assumed to occur independently.This kind of document representation ignores relationships between different words.To capture the important relationships between words

we try to learn a ranking model using the topic features of documents and queries.We define the ranking function as the topic relations between a document and a query.A novel rank learning algorithm based on supervised topic model is proposed to learn the ranking function.To evaluate the ranking accuracy of the proposed ranking algorithm

experiments are made on three benchmark datasets for information retrieval

OHSUMED

MQ2007

and MQ2008.The experimental results show that the proposed model can find the semantic relation between a document and a query

and can improve the ranking accuracy.