Scalable and Efficient Comparison-based Search without Features
This addresses the challenge of efficient search without direct feature access, which is incremental as it builds on existing comparison-based methods but introduces a scalable framework for blind settings.
The paper tackles the problem of finding a target object using pairwise comparisons in both non-blind (with features) and blind (without features) settings, proposing a Bayesian search algorithm and a learning framework called Learn2Search that achieves query complexity on par with the non-blind setting on real-world datasets.
We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"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 {\em 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 \emph{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 \textsc{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-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting an experiment with users searching for movie actors.