Cross-Domain Ranking via Latent Space Learning

Abstract

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.