Speeding up Large-Scale Learning with a Social Prior

ACM Conference on Knowledge Discovery and Data Mining (KDD)


Slow convergence and poor initial accuracy are two problems that plague efforts to use very large feature sets in online learning. This is especially true when only a few features are ‘active’ in any training example, and the frequency of activations of different features is skewed. We show how these problems can be mitigated if a graph of relationships between features is known.

We study this problem in a fully Bayesian setting, focusing on the problem of using Facebook user-IDs as features, with the social network giving the relationship structure. Our analysis uncovers significant problems with the obvious regularizations, and motivates a two-component mixture-model ‘social prior’ that is provably better.

Empirical results on large-scale click prediction problems show that our algorithm can learn as well as the baseline with 12M fewer training examples, and continuously outperforms it for over 60M examples.

On a second problem using binned features, our model outperforms the baseline even after the latter sees 5x as much data.

Featured Publications