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CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation

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 des...

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Détails bibliographiques
Auteurs principaux: Ming He, Boyang An, Jiwen Wang, Hao Wen
Format: Artigo
Langue:Inglês
Publié: MDPI AG 2023-10-01
Collection:Applied Sciences
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Accès en ligne:https://www.mdpi.com/2076-3417/13/20/11176
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