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The project is about providing counterfactual explanations for biased recommendation results. The developed algorithms produce explanations efficiently for the case of random walk-based recommenders. The scenario tested in the experiments is user-movie ratings, but the algorithms can be applied in any user-item interactions scheme.
This project aims to implement the Multi-task-Stacked-Bi-LSTMs applied in detecting the span of the counterfactual statement using ELMo Word Embedding and POS tags.