Multi-task Learning for Recommender System
Xia Ning (University of Minnesota) and George Karypis (University
JMLR W&P 13:269-284, 2010.
This paper focuses on exploring personalized multi-task learning approaches
for collaborative filtering towards the goal of improving the
prediction performance of rating prediction systems. These methods
first specifically identify a set of users that are closely related to the
user under consideration (i.e., active user), and then learn multiple rating
prediction models simultaneously, one for the active user and one
for each of the related users. Such learning for multiple models (tasks)
in parallel is implemented by representing all learning instances (users
and items) using a coupled user-item representation, and within errorinsensitive
Support Vector Regression (e-SVR) framework applying
multi-task kernel tricks. A comprehensive set of experiments shows
that multi-task learning approaches lead to significant performance
improvement over conventional alternatives.