A noise-tolerant graphical model for ranking

This paper studies how to learn accurate ranking functions from noisy training data for information retrieval. Most previous work on learning to rank assumes that the relevance labels in the training data are reliable. In reality, however, the labels usually contain noise due to the difficulties of relevance judgments and several other reasons. To tackle the problem, in this paper we propose a novel approach to learning to rank, based on a probabilistic graphical model. Considering that the observed label might be noisy, we introduce a new variable to indicate the true label of each instance. We then use a graphical model to capture the joint distribution of the true labels and observed labels given features of documents. The graphical model distinguishes the true labels from observed labels, and is specially designed for ranking in information retrieval. Therefore, it helps to learn a more accurate model from noisy training data. Experiments on a real dataset for web search show that the proposed approach can significantly outperform previous approaches.

► We have proposed a probabilistic graphical model to learn from noisy training data in ranking.
► We have conducted efficient learning and inference for the proposed model.
► We have empirically verified the effectiveness of the proposed model on a large dataset from a commercial search engine.