کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
388549 | 660926 | 2011 | 6 صفحه PDF | دانلود رایگان |

Learning to rank, a task to learn ranking functions to sort a set of entities using machine learning techniques, has recently attracted much interest in information retrieval and machine learning research. However, most of the existing work conducts a supervised learning fashion. In this paper, we propose a transductive method which extracts paired preference information from the unlabeled test data. Then we design a loss function to incorporate this preference data with the labeled training data, and learn ranking functions by optimizing the loss function via a derived Ranking SVM framework. The experimental results on the LETOR 2.0 benchmark data collections show that our transductive method can significantly outperform the state-of-the-art supervised baseline.
► We propose a transductive method for learning to rank.
► We design a loss function to incorporate the information from labeled and unlabeled data.
► The experimental results show that our method outperforms the supervised baseline.
Journal: Expert Systems with Applications - Volume 38, Issue 10, 15 September 2011, Pages 12839–12844