  The recommendation system, relying on historical observational data to model
the complex relationships among the users and items, has achieved great success
in real-world applications. Selection bias is one of the most important issues
of the existing observational data based approaches, which is actually caused
by multiple types of unobserved exposure strategies (e.g. promotions and
holiday effects). Though various methods have been proposed to address this
problem, they are mainly relying on the implicit debiasing techniques but not
explicitly modeling the unobserved exposure strategies. By explicitly
Reconstructing Exposure STrategies (REST in short), we formalize the
recommendation problem as the counterfactual reasoning and propose the debiased
social recommendation method. In REST, we assume that the exposure of an item
is controlled by the latent exposure strategies, the user, and the item. Based
on the above generation process, we first provide the theoretical guarantee of
our method via identification analysis. Second, we employ a variational
auto-encoder to reconstruct the latent exposure strategies, with the help of
the social networks and the items. Third, we devise a counterfactual reasoning
based recommendation algorithm by leveraging the recovered exposure strategies.
Experiments on four real-world datasets, including three published datasets and
one private WeChat Official Account dataset, demonstrate significant
improvements over several state-of-the-art methods.
