Multi-task learning for recommender systems

Xia Ning, George Karypis

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

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 error-insensitive Support Vector Regression (ε-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.

Original languageEnglish (US)
Pages (from-to)269-284
Number of pages16
JournalJournal of Machine Learning Research
Volume13
StatePublished - Dec 1 2010
Event2nd Asian Conference on Machine Learning, ACML 2010 - Tokyo, Japan
Duration: Nov 8 2010Nov 10 2010

Keywords

  • Collaborative filtering
  • Multi-task learning

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