A unified posterior regularized topic model with maximum margin for learning-to-rank

While most methods for learning-to-rank documents only consider relevance scores as features, better results can often be obtained by taking into account the latent topic structure of the document collection. Existing approaches that consider latent topics follow a two-stage approach, in which topics are discovered in an unsupervised way, as usual, and then used as features for the learning-to-rank task. In contrast, we propose a learning-to-rank framework which integrates the supervised learning of a maximum margin classifier with the discovery of a suitable probabilistic topic model. In this way, the labelled data that is available for the learning-to-rank task can be exploited to identify the most appropriate topics. To this end, we use a unified constrained optimization framework, which can dynamically compute the latent topic similarity score between the query and the document. Our experimental results show a consistent improvement over the state-of-the-art learning-to-rank models