We define a condition that is specific to categorical-set features -- defined as an unordered set of categorical variables -- and present an algorithm to learn it, thereby equipping decision forests with the ability to directly model text, albeit without preserving sequential order.
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data.
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval.
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.