Abstract
Explanations can substantially enhance users’ trust and satisfaction with recommender systems. Counterfactual explanations have demonstrated remarkable effectiveness in enhancing the performance of explainable sequential recommendation. However, existing counterfactual explanation models for sequential recommendation ignore temporal dependencies in a user’s historical behavior sequence. Moreover, counterfactual histories must be as close as possible to the real history; otherwise, they will violate the user’s real behavioral preferences. In this paper, we propose Counterfactual Explanations with Temporal Dependencies (CETD), a counterfactual explanation model based on a Variational Autoencoder (VAE) for sequential recommendation that handles temporal dependencies. When generating counterfactual histories, CETD uses a Recurrent Neural Network (RNN) to capture both long-term preferences and short-term behavior in the user’s real behavioral history, which can enhance explainability. Meanwhile, CETD fits the distribution of reconstructed data in a latent space, and then uses the variance obtained from learning to make counterfactual sequences closer to the original sequence, which will reduce the proximity of counterfactual histories. Extensive experiments on two real-world datasets show that the proposed CETD consistently outperforms state-of-the-art methods.
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He, M., An, B., Wang, J., Wen, H. (2023). Counterfactual Explanations for Sequential Recommendation with Temporal Dependencies. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_41
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