  Recommender systems may be confounded by various types of confounding factors
(also called confounders) that may lead to inaccurate recommendations and
sacrificed recommendation performance. Current approaches to solving the
problem usually design each specific model for each specific confounder.
However, real-world systems may include a huge number of confounders and thus
designing each specific model for each specific confounder could be
unrealistic. More importantly, except for those ``explicit confounders'' that
experts can manually identify and process such as item's position in the
ranking list, there are also many ``latent confounders'' that are beyond the
imagination of experts. For example, users' rating on a song may depend on
their current mood or the current weather, and users' preference on ice creams
may depend on the air temperature. Such latent confounders may be unobservable
in the recorded training data. To solve the problem, we propose Deconfounded
Causal Collaborative Filtering (DCCF). We first frame user behaviors with
unobserved confounders into a causal graph, and then we design a front-door
adjustment model carefully fused with machine learning to deconfound the
influence of unobserved confounders. Experiments on real-world datasets show
that our method is able to deconfound unobserved confounders to achieve better
recommendation performance.
