  Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings, all the items, including the ones
that a user did not consume, are taken into consideration. But this assumption
does not accord with the common sense understanding that users have a limited
scope and awareness of items. For example, a user might not have heard of a
certain paper, or might live too far away from a restaurant to experience it.
In the language of causal analysis, the assignment mechanism (i.e., the items
that a user is exposed to) is a latent variable that may change for various
user/item combinations. In this paper, we propose a new probabilistic approach
that directly incorporates user exposure to items into collaborative filtering.
The exposure is modeled as a latent variable and the model infers its value
from data. In doing so, we recover one of the most successful state-of-the-art
approaches as a special case of our model, and provide a plug-in method for
conditioning exposure on various forms of exposure covariates (e.g., topics in
text, venue locations). We show that our scalable inference algorithm
outperforms existing benchmarks in four different domains both with and without
exposure covariates.
