Scalable and Efficient Comparison-based Search without Features

Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser

Abstract:

We consider the problem of finding a target object
t using pairwise comparisons, by asking an oracle
questions of the form “Which object from the pair
(i, j) is more similar to t?”. Objects live in a space
of latent features, from which the oracle generates
noisy answers. First, we consider the non-blind
setting where these features are accessible. We
propose a new Bayesian comparison-based search
algorithm with noisy answers; it has low computational complexity yet is efficient in the number
of queries. We provide theoretical guarantees, deriving the form of the optimal query and proving
almost sure convergence to the target t. Second,
we consider the blind setting, where the object
features are hidden from the search algorithm.
In this setting, we combine our search method
and a new distributional triplet embedding algorithm into one scalable learning framework called
LEARN2SEARCH. We show that the query complexity of our approach on two real-world datasets
is on par with the non-blind setting, which is not
achievable using any of the current state-of-theart embedding methods. Finally, we demonstrate
the efficacy of our framework by conducting an
experiment with users searching for movie actors.