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Panagiotis Karras

Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging

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  • Nikita Klyuchnikov, Skoltech, Rusland
  • Davide Mottin
  • Georgia Koutrika, "Athena" Research Center, Grækenland
  • Emmanuel Müller, University of Bonn, Tyskland
  • Panagiotis Karras

Can a system discover what a user wants without the user explicitly issuing a query? A recommender system proposes items of potential interest based on past user history. On the other hand, active search incites, and learns from, user feedback, in order to recommend items that meet a user's current tacit interests, hence promises to offer up-to-date recommendations going beyond those of a recommender system. Yet extant active search methods require an overwhelming amount of user input, relying solely on such input for each item they pick. In this paper, we propose MF-ASC, a novel active search mechanism that performs well with minimal user input. MF-ASC combines cheap, low-fidelity evaluations in the style of a recommender system with the user's high-fidelity input, using Gaussian process regression with multiple target variables (cokriging). To our knowledge, this is the first application of cokriging to active search. Our empirical study with synthetic and real-world data shows that MF-ASC outperforms the state of the art in terms of result relevance within a budget of interactions.

TitelKDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Antal sider10
ForlagAssociation for Computing Machinery
Udgivelsesåraug. 2019
StatusUdgivet - aug. 2019
Begivenhed25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Dena’ina Convention Center and William Egan Convention Center, Anchorage, USA
Varighed: 4 aug. 20198 aug. 2019
Konferencens nummer: 25


Konference25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
LokationDena’ina Convention Center and William Egan Convention Center

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