Focused Marix Factorization for Audience Selection in Display Advertising
Venue
Proceedings of the 29th International Conference on Data Engineering (ICDE) (2013)
Publication Year
2013
Authors
Bhargav Kanagal, Amr Ahmed, Sandeep Pandey, Vanja Josifovski, Lluis Garcia-Pueyo, Jeff Yuan
BibTeX
Abstract
Audience selection is a key problem in display advertising systems in which we need
to select a list of users who are interested (i.e., most likely to buy) in an
advertising campaign. The users’ past feedback on this campaign can be leveraged to
construct such a list using collaborative filtering techniques such as matrix
factorization. However, the user-campaign interaction is typically extremely
sparse, hence the conventional matrix factorization does not perform well.
Moreover, simply combining the users feedback from all campaigns does not address
this since it dilutes the focus on target campaign in consideration. To resolve
these issues, we propose a novel focused matrix factorization model (FMF) which
learns users’ preferences towards the specific campaign products, while also
exploiting the information about related products. We exploit the product taxonomy
to discover related campaigns, and design models to discriminate between the users’
interest towards campaign products and non-campaign products. We develop a parallel
multi-core implementation of the FMF model and evaluate its performance over a
real-world advertising dataset spanning more than a million products. Our
experiments demonstrate the benefits of using our models over existing approaches.
