CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
Ming He,
Boyang An,
Jiwen Wang
et al.
Abstract:Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s past behavior sequence. Furthermore, counterfactual histories should be as similar to the real history as possible to avoid conflict… Show more
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