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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References
Cai, R., Wu, J., San, A., Wang, C., Wang, H.: Category-aware collaborative sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 388–397 (2021)
Chang, J., et al.: Sequential recommendation with graph neural networks. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 378–387 (2021)
Cheng, W., Shen, Y., Huang, L., Zhu, Y.: Incorporating interpretability into latent factor models via fast influence analysis. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 885–893 (2019)
Fu, Z., et al.: Fairness-aware explainable recommendation over knowledge graphs. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 69–78 (2020)
Geng, S., Fu, Z., Tan, J., Ge, Y., De Melo, G., Zhang, Y.: Path language modeling over knowledge graphs for explainable recommendation. In: Proceedings of the ACM Web Conference 2022, pp. 946–955 (2022)
Ghazimatin, A., Balalau, O., Saha Roy, R., Weikum, G.: PRINCE: provider-side interpretability with counterfactual explanations in recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 196–204 (2020)
Gholami, E., Motamedi, M., Aravindakshan, A.: PARSRec: explainable personalized attention-fused recurrent sequential recommendation using session partial actions. arXiv preprint arXiv:2209.13015 (2022)
He, R., McAuley, J.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 191–200. IEEE (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Hou, H., Shi, C.: Explainable sequential recommendation using knowledge graphs. In: Proceedings of the 5th International Conference on Frontiers of Educational Technologies, pp. 53–57 (2019)
Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.R.: Towards universal sequence representation learning for recommender systems. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 585–593 (2022)
Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y., Xu, C.: Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 548–556 (2019)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Li, Y., Chen, H., Li, Y., Li, L., Philip, S.Y., Xu, G.: Reinforcement learning based path exploration for sequential explainable recommendation. IEEE Trans. Knowl. Data Eng. (2023)
Liu, Z., Chen, Y., Li, J., Yu, P.S., McAuley, J., Xiong, C.: Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021)
Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X., Coates, M.: Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5045–5052 (2020)
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)
Peake, G., Wang, J.: Explanation mining: post hoc interpretability of latent factor models for recommendation systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2060–2069 (2018)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)
Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Tan, J., et al.: Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In: Proceedings of the ACM Web Conference 2022, pp. 1018–1027 (2022)
Tan, J., Xu, S., Ge, Y., Li, Y., Chen, X., Zhang, Y.: Counterfactual explainable recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1784–1793 (2021)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)
Tran, K.H., Ghazimatin, A., Saha Roy, R.: Counterfactual explanations for neural recommenders. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1627–1631 (2021)
Wang, N., Wang, H., Jia, Y., Yin, Y.: Explainable recommendation via multi-task learning in opinionated text data. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 165–174 (2018)
Xu, C., et al.: Recurrent convolutional neural network for sequential recommendation. In: The World Wide Web Conference, pp. 3398–3404 (2019)
Xu, S., Li, Y., Liu, S., Fu, Z., Chen, X., Zhang, Y.: Learning post-hoc causal explanations for recommendation. arXiv preprint arXiv:2006.16977 (2020)
Xu, S., et al.: Learning causal explanations for recommendation. In: The 1st International Workshop on Causality in Search and Recommendation (2021)
Yang, A., Wang, N., Cai, R., Deng, H., Wang, H.: Comparative explanations of recommendations. In: Proceedings of the ACM Web Conference 2022, pp. 3113–3123 (2022)
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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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