Half Transductive Ranking
Venue
Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Publication Year
2010
Authors
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri
BibTeX
Abstract
We study the standard retrieval task of ranking a fixed set of items given a
previously unseen query and pose it as the half transductive ranking problem. The
task is transductive as the set of items is fixed. Transductive representations
(where the vector representation of each example is learned) allow the generation
of highly nonlinear embeddings that capture object relationships without relying on
a specific choice of features, and require only relatively simple optimization.
Unfortunately, they have no direct outof- sample extension. Inductive approaches on
the other hand allow for the representation of unknown queries. We describe
algorithms for this setting which have the advantages of both transductive and
inductive approaches, and can be applied in unsupervised (either
reconstruction-based or graph-based) and supervised ranking setups. We show
empirically that our methods give strong performance on all three tasks.
