Half Transductive Ranking
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.
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.