TL;DR

Efficient interactive method to learn user preferences in an active way:

• Fast – Only 10 steps to build a model of the user
• Multiple information – incorporate shallow preferences from the system to help the user
• Flexible – recommendations for multi-dimensional and graph data

In this paper:

• A novel active search formulation that fuses continuous user scores with correlated computationally derived scores
• Experiments on synthetic data, in which MF-ASC outperforms multifidelity methods for function optimization
• Two real case-studies on tabular consumer ratings and information graphs.
• Real-user experiments in which MF-ASC outperforms state-of-the-art single-fidelity active search methods
• A general and powerful method that adapts to different data models and situations
• Few interactions steps with the user capture their preference starting from scratch