Learning to rank or machine-learned ranking (MLR) is a type of supervised or semi-supervised machine learning problem in which the goal is to automatically construct a ranking model from training data. LTR(Learning torank)学习排序是一种监督学习(SupervisedLearning)的排序方法。LTR已经被广泛应用到文本挖掘的很多领域,比如IR中排序返回的文档,推荐系统中的候选产品、用户排序,机器翻译中排序候选翻译结果等等。