This work is licensed under the Creative Commons Attribution 4.0 International License.
Das, D., Sahoo, L. and Datta, S., 2017. A survey on recommendation system. International Journal of Computer Applications, 160(7).Search in Google Scholar
Fang, H., Zhang, D., Shu, Y. and Guo, G., 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS), 39(1), pp.1-42.Search in Google Scholar
Cheng, M., Liu, Z., Liu, Q., Ge, S. and Chen, E., 2022, April. Towards automatic discovering of deep hybrid network architecture for sequential recommendation. In Proceedings of the ACM Web Conference 2022 (pp. 1923-1932).Search in Google Scholar
Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z. and Orgun, M., 2019. Sequential recommender systems: challenges, progress and prospects. arXiv preprint arXiv:2001.04830.Search in Google Scholar
Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Xiong, H. and Wu, J., 2018, January. Sequential recommender system based on hierarchical attention network. In IJCAI International Joint Conference on Artificial Intelligence.Search in Google Scholar
Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., Jin, D. and Li, Y., 2021, July. 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).Search in Google Scholar
Ghazimatin, A., Balalau, O., Saha Roy, R. and Weikum, G., 2020, January. 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).Search in Google Scholar
Liu, Y., Ge, K., Zhang, X. and Lin, L., 2019, July. Real-time attention based look-alike model for recommender system. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2765-2773).Search in Google Scholar
Ren, Q.J., Zhao, X. and Han, Y., 2021. Analysis on the causes of the information cocoons under user perspectives. Library and Information Service, 65(01), pp.120-127.Search in Google Scholar
Bian, S., Zhao, W.X., Wang, J. and Wen, J.R., 2022, October. A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 2923-2932).Search in Google Scholar
Wang, W., Feng, F., He, X., Wang, X. and Chua, T.S., 2021, August. Deconfounded recommendation for alleviating bias amplification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1717-1725).Search in Google Scholar
Rendle, S., Freudenthaler, C. and Schmidt-Thieme, L., 2010, April. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web (pp. 811-820).Search in Google Scholar
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T. and Ma, J., 2017, November. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1419-1428).Search in Google Scholar
Tang, J. and Wang, K., 2018, February. 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).Search in Google Scholar
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W. and Jiang, P., 2019, November. 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).Search in Google Scholar
Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z. and Zha, H., 2018, February. Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 108-116).Search in Google Scholar
Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X. and Coates, M., 2020, April. Memory augmented graph neural networks for sequential recommendation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 5045-5052).Search in Google Scholar
Hidasi, B., Karatzoglou, A., Baltrunas, L. and Tikk, D., 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.Search in Google Scholar
Liu, Q., Zeng, Y., Mokhosi, R. and Zhang, H., 2018, July. STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1831-1839).Search in Google Scholar
Zhao, X., Zhang, L., Xia, L., Ding, Z., Yin, D. and Tang, J., 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209.Search in Google Scholar
Qiu, R., Huang, Z., Yin, H. and Wang, Z., 2022, February. Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 813-823).Search in Google Scholar
Ni, S., Zhou, W., Wen, J., Hu, L. and Qiao, S., 2023. Enhancing sequential recommendation with contrastive Generative Adversarial Network. Information Processing & Management, 60(3), p.103331.Search in Google Scholar
Chen, G., Zhao, G., Zhu, L., Zhuo, Z. and Qian, X., 2022. Combining non-sampling and self-attention for sequential recommendation. Information Processing & Management, 59(2), p.102814.Search in Google Scholar
Li, Y., Luo, Y., Zhang, Z., Sadiq, S. and Cui, P., 2019, April. Context-aware attention-based data augmentation for POI recommendation. In 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW) (pp. 177-184). IEEE.Search in Google Scholar
Tran, K.H., Ghazimatin, A. and Saha Roy, R., 2021, July. Counterfactual explanations for neural recommenders. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1627-1631).Search in Google Scholar
Tan, J., Xu, S., Ge, Y., Li, Y., Chen, X. and Zhang, Y., 2021, October. Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 1784-1793).Search in Google Scholar
Li, Y., Chen, H., Xu, S., Ge, Y. and Zhang, Y., 2021. Personalized Counterfactual Fairness in Recommendation. arXiv preprint arXiv:2105.09829.Search in Google Scholar
Zhan, R., Pei, C., Su, Q., Wen, J., Wang, X., Mu, G., Zheng, D., Jiang, P. and Gai, K., 2022, August. Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4472-4481).Search in Google Scholar
Liu, H., Tang, D., Yang, J., Zhao, X., Liu, H., Tang, J. and Cheng, Y., 2022, April. Rating distribution calibration for selection bias mitigation in recommendations. In Proceedings of the ACM Web Conference 2022 (pp. 2048-2057).Search in Google Scholar
Liu, D., Cheng, P., Zhu, H., Dong, Z., He, X., Pan, W. and Ming, Z., 2021, September. Mitigating confounding bias in recommendation via information bottleneck. In Proceedings of the 15th ACM Conference on Recommender Systems (pp. 351-360).Search in Google Scholar
Wang, Z., Chen, X., Zhou, R., Dai, Q., Dong, Z. and Wen, J.R., 2023, April. Sequential Recommendation with User Causal Behavior Discovery. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) (pp. 28-40). IEEE.Search in Google Scholar
Wang, Z., Zhang, J., Xu, H., Chen, X., Zhang, Y., Zhao, W.X. and Wen, J.R., 2021, July. Counterfactual data-augmented sequential recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 347-356).Search in Google Scholar
Luo, H., Zhuang, F., Xie, R., Zhu, H., Wang, D., An, Z. and Xu, Y., 2023. A survey on causal inference for recommendation. The Innovation.Search in Google Scholar
Gao, C., Zheng, Y., Wang, W., Feng, F., He, X. and Li, Y., 2022. Causal inference in recommender systems: A survey and future directions. ACM Transactions on Information Systems.Search in Google Scholar
Keith, K.A., Jensen, D. and O’Connor, B., 2020. Text and causal inference: A review of using text to remove confounding from causal estimates. arXiv preprint arXiv:2005.00649.Search in Google Scholar
Yang, X., Feng, F., Ji, W., Wang, M. and Chua, T.S., 2021, July. Deconfounded video moment retrieval with causal intervention. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1-10).Search in Google Scholar
Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S. and Zhuang, Y., 2020. Counterfactual samples synthesizing for robust visual question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10800-10809).Search in Google Scholar
Tishby, N., Pereira, F.C. and Bialek, W., 2000. The information bottleneck method. arXiv preprint physics/0004057.Search in Google Scholar
Mu, S., Li, Y., Zhao, W.X., Wang, J., Ding, B. and Wen, J.R., 2022, July. Alleviating spurious correlations in knowledge-aware recommendations through counterfactual generator. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1401-1411).Search in Google Scholar
Wang, Z., Chen, X., Wen, R., Huang, S.L., Kuruoglu, E. and Zheng, Y., 2020. Information theoretic counterfactual learning from missing-not-at-random feedback. Advances in Neural Information Processing Systems, 33, pp.1854-1864.Search in Google Scholar
Zheng, Y., Tang, B., Ding, W. and Zhou, H., 2016, June. A neural autoregressive approach to collaborative filtering. In International Conference on Machine Learning (pp. 764-773). PMLR.Search in Google Scholar
He, R., Lin, C., Wang, J. and McAuley, J., 2016. Sherlock: sparse hierarchical embeddings for visually-aware one-class collaborative filtering. arXiv preprint arXiv:1604.05813.Search in Google Scholar
Hidasi, B., Karatzoglou, A., Baltrunas, L. and Tikk, D., 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.Search in Google Scholar
Kang, W.C. and McAuley, J., 2018, November. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) (pp. 197-206). IEEE.Search in Google Scholar
Liu, C., Li, X., Cai, G., Dong, Z., Zhu, H. and Shang, L., 2021, May. Noninvasive self-attention for side information fusion in sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4249-4256).Search in Google Scholar
Xie, Y., Zhou, P. and Kim, S., 2022, July. Decoupled side information fusion for sequential recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1611-1621).Search in Google Scholar
Li, C., Liu, Z., Wu, M., Xu, Y., Zhao, H., Huang, P., Kang, G., Chen, Q., Li, W. and Lee, D.L., 2019, November. Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 2615-2623).Search in Google Scholar
Zhang, S., Yao, D., Zhao, Z., Chua, T.S. and Wu, F., 2021, July. Causerec: Counterfactual user sequence synthesis for sequential recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 367-377).Search in Google Scholar
Zheng, Y., Gao, C., Li, X., He, X., Li, Y. and Jin, D., 2021, April. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference 2021 (pp. 2980-2991).Search in Google Scholar
Zhang, C., Bauer, S., Bennett, P., Gao, J., Gong, W., Hilmkil, A., Jennings, J., Ma, C., Minka, T., Pawlowski, N. and Vaughan, J., 2023. Understanding causality with large language models: Feasibility and opportunities. arXiv preprint arXiv:2304.05524.Search in Google Scholar
Jiang, H., Ge, L., Gao, Y., Wang, J. and Song, R., 2023. Large Language Model for Causal Decision Making. arXiv preprint arXiv:2312.17122.Search in Google Scholar