Collaborative Expert Portfolio Management

We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement ...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella
• Keywords: AAAI 2010, Decision Theoretic Framework, Expert, Intelligent Agents, Model