Microsoft Corporation
LEARNING TO RANK WITH ALPHA DIVERGENCE AND ENTROPY REGULARIZATION

Abstract:

In an example embodiment, .alpha.-divergence is used to replace cross-entropy or KL-divergence as the loss function for learning-to-rank tasks in an online network. Additionally, in an example embodiment, entropy regularization is used to encourage score diversity for documents of the same relevance level. The result of both these approaches it to reduce or eliminate technical problems encountered using prior art techniques.