CLEAR: Causal Explanations from Attention in Neural Recommenders
This work addresses the need for interpretable and causal explanations in recommender systems, which is an incremental improvement over existing attention-based methods.
The paper tackles the problem of providing counterfactual explanations for recommendations in attention-based neural recommenders by learning session-specific causal graphs, even with latent confounders, and shows that CLEAR produces shorter explanations and ranks alternative recommendations higher compared to naive attention weight methods.
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders. These causal graphs describe user behavior, within the context captured by attention, and can provide a counterfactual explanation for a recommendation. In essence, these causal graphs allow answering "why" questions uniquely for any specific session. Using empirical evaluations we show that, compared to naively using attention weights to explain input-output relations, counterfactual explanations found by CLEAR are shorter and an alternative recommendation is ranked higher in the original top-k recommendations.