  Players in the online ad ecosystem are struggling to acquire the user data
required for precise targeting. Audience look-alike modeling has the potential
to alleviate this issue, but models' performance strongly depends on quantity
and quality of available data. In order to maximize the predictive performance
of our look-alike modeling algorithms, we propose two novel hybrid filtering
techniques that utilize the recent neural probabilistic language model
algorithm doc2vec. We apply these methods to data from a large mobile ad
exchange and additional app metadata acquired from the Apple App store and
Google Play store. First, we model mobile app users through their app usage
histories and app descriptions (user2vec). Second, we introduce context
awareness to that model by incorporating additional user and app-related
metadata in model training (context2vec). Our findings are threefold: (1) the
quality of recommendations provided by user2vec is notably higher than current
state-of-the-art techniques. (2) User representations generated through hybrid
filtering using doc2vec prove to be highly valuable features in supervised
machine learning models for look-alike modeling. This represents the first
application of hybrid filtering user models using neural probabilistic language
models, specifically doc2vec, in look-alike modeling. (3) Incorporating context
metadata in the doc2vec model training process to introduce context awareness
has positive effects on performance and is superior to directly including the
data as features in the downstream supervised models.
