Private Matrix Factorization with Public Item Features
Mihaela Curmei Walid Krichene Li Zhang Mukund Sundararajan
We consider the problem of training private recommendation models with access
to public item features. Training with Differential Privacy (DP) offers strong
privacy guarantees, at the expense of loss in recommendation quality. We show
that incorporating public item features during training can help mitigate this
loss in quality. We propose a general approach based on collective matrix
factorization (CMF), that works by simultaneously factorizing two matrices: the
user feedback matrix (representing sensitive data) and an item feature matrix
that encodes publicly available (non-sensitive) item information.
  The method is conceptually simple, easy to tune, and highly scalable. It can
be applied to different types of public item data, including: (1) categorical
item features; (2) item-item similarities learned from public sources; and (3)
publicly available user feedback. Furthermore, these data modalities can be
collectively utilized to fully leverage public data.
  Evaluating our method on a standard DP recommendation benchmark, we find that
using public item features significantly narrows the quality gap between
private models and their non-private counterparts. As privacy constraints
become more stringent, models rely more heavily on public side features for
recommendation. This results in a smooth transition from collaborative
filtering to item-based contextual recommendations.