Scalable and Efficient Comparison-based Search without Features

Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1995-2005, 2020.

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-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting a movie actors search experiment with real users.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-chumbalov20a, title = {Scalable and Efficient Comparison-based Search without Features}, author = {Chumbalov, Daniyar and Maystre, Lucas and Grossglauser, Matthias}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {1995--2005}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/chumbalov20a/chumbalov20a.pdf}, url = {https://proceedings.mlr.press/v119/chumbalov20a.html}, 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-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting a movie actors search experiment with real users.} }
Endnote
%0 Conference Paper %T Scalable and Efficient Comparison-based Search without Features %A Daniyar Chumbalov %A Lucas Maystre %A Matthias Grossglauser %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-chumbalov20a %I PMLR %P 1995--2005 %U https://proceedings.mlr.press/v119/chumbalov20a.html %V 119 %X 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-the-art embedding methods. Finally, we demonstrate the efficacy of our framework by conducting a movie actors search experiment with real users.
APA
Chumbalov, D., Maystre, L. & Grossglauser, M.. (2020). Scalable and Efficient Comparison-based Search without Features. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:1995-2005 Available from https://proceedings.mlr.press/v119/chumbalov20a.html.

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