Inventors:
- Redmond WA, US
Xiao Yan - Sunnyvale CA, US
Lin Zhu - Santa Clara CA, US
Jaewon Yang - Sunnyvale CA, US
Yanen Li - Foster City CA, US
Jacob Bollinger - San Francisco CA, US
International Classification:
G06F 16/9535
G06N 3/04
G06F 16/9536
G06F 16/36
G06N 20/20
Abstract:
Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.
