Title
Collaborative Expert Portfolio Management
Abstract
We consider the task of assigning experts from a port- folio 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 affin- ity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task character- istics. The approach allows us to use a principled de- cision theoretic framework for expert selection, allow- ing 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, al- lowing flexibility. We apply the model to manage a port- folio of algorithms to solve hard combinatorial prob- lems. This is a well studied area and we demonstrate a large improvement on the state of the art in one do- main (constraint solving) and in a second domain (com- binatorial auctions) created a portfolio that performed significantly better than any single algorithm.
Year
Venue
Keywords
2010
AAAI
bayesian model,machine learning,linear programming,inner product,collaborative filtering,portfolio management,recommender system
Field
DocType
Citations 
Collaborative filtering,Embedding,Bayesian inference,Project portfolio management,Computer science,Combinatorial auction,Portfolio,Artificial intelligence,Performance feedback,Machine learning
Conference
13
PageRank 
References 
Authors
0.73
15
6
Name
Order
Citations
PageRank
David H. Stern11439.99
Horst Samulowitz231626.05
Ralf Herbrich31660170.61
Thore Graepel44211242.71
Luca Pulina532637.95
Armando Tacchella61448108.82