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

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.