"diffDebias" Architecture

In this approach for the debiasing of the recommendation system, we formalize an end-to-end differentiable approach that can be integrated into the temporally evolving graph as a recommendation engine,. In this architecture, we propose a disentanglement-inspired approach to redistribute the observed link prediction distribution to ideal distribution, as we claim that the exposure mechanism of the recommendation engine becomes a source of biased recommendations, the implicit biases generate a distribution of the frequencies between user-item nodes which differ from the actual ideal distribution, which then gets amplified in the feedback loop.

Using this architecture we aim to remap the biased distribution to an ideal one, by exploiting the latent representation of the graph and propose an encoder-decoder-based debiasing framework.