CUED Publications database

Cold-start active learning with robust ordinal matrix factorization

Houlsby, N and Hernández-Lobato, JM and Ghahramani, Z (2014) Cold-start active learning with robust ordinal matrix factorization. In: UNSPECIFIED pp. 1964-1972..

Full text not available from this repository.


Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. We present a new matrix factorization model for rating data and a corresponding active learning strategy to address the cold-start problem. Cold- start is one of the most challenging tasks for rec- ommender systems: what to recommend with new users or items for which one has little or no data. An approach is to use active learning to collect the most useful initial ratings. However, the performance of active learning depends strongly upon having accurate estimates of i) the uncertainty in model parameters and ii) the intrinsic noisiness of the data. To achieve these estimates we propose a heteroskedastic Bayesian model for ordinal matrix factorization. We also present a computationally efficient framework for Bayesian active learning with this type of complex probabilistic model. This algorithm successfully distinguishes between informative and noisy data points. Our model yields state-of- the-art predictive performance and, coupled with our active learning strategy, enables us to gain useful information in the cold-start setting from the very first active sample.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:45
Last Modified: 18 Aug 2020 12:13