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On the Effectiveness of Unlearning in Session-Based Recommendation (2312.14447v1)

Published 22 Dec 2023 in cs.IR and cs.AI

Abstract: Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain training samples, also occurs for reasons such as user privacy or model fidelity. However, existing studies on unlearning are not tailored for the session-based recommendation. On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session. On the other hand, seldom work has conducted the research to verify the unlearning effectiveness in the session-based recommendation scenario. In this paper, we propose SRU, a session-based recommendation unlearning framework, which enables high unlearning efficiency, accurate recommendation performance, and improved unlearning effectiveness in session-based recommendation. Specifically, we first partition the training sessions into separate sub-models according to the similarity across the sessions, then we utilize an attention-based aggregation layer to fuse the hidden states according to the correlations between the session and the centroid of the data in the sub-model. To improve the unlearning effectiveness, we further propose three extra data deletion strategies, including collaborative extra deletion (CED), neighbor extra deletion (NED), and random extra deletion (RED). Besides, we propose an evaluation metric that measures whether the unlearning sample can be inferred after the data deletion to verify the unlearning effectiveness. We implement SRU with three representative session-based recommendation models and conduct experiments on three benchmark datasets. Experimental results demonstrate the effectiveness of our methods.

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References (43)
  1. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 141–159.
  2. Jonathan Brophy and Daniel Lowd. 2021. Machine unlearning for random forests. In International Conference on Machine Learning. PMLR, 1092–1104.
  3. Yinzhi Cao and Junfeng Yang. 2015. Towards making systems forget with machine unlearning. In 2015 IEEE symposium on security and privacy. IEEE, 463–480.
  4. Extracting Training Data from Large Language Models. CoRR abs/2012.07805 (2020). arXiv:2012.07805
  5. Recommendation unlearning. In Proceedings of the ACM Web Conference 2022. 2768–2777.
  6. GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models (CCS ’20). Association for Computing Machinery, New York, NY, USA.
  7. Lightweight machine unlearning in neural network. arXiv:2111.05528 [cs.LG]
  8. Graph Unlearning. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. ACM. https://doi.org/10.1145/3548606.3559352
  9. Sequential User-Based Recurrent Neural Network Recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 152–160. https://doi.org/10.1145/3109859.3109877
  10. Certified Data Removal from Machine Learning Models. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119), Hal Daumé III and Aarti Singh (Eds.). PMLR, 3832–3842.
  11. Adaptive Machine Unlearning. arXiv:2106.04378 [cs.LG]
  12. Translation-Based Recommendation (RecSys ’17). Association for Computing Machinery.
  13. Ruining He and Julian McAuley. 2016. Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 191–200. https://doi.org/10.1109/ICDM.2016.0030
  14. Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM. https://doi.org/10.1145/3269206.3271761
  15. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
  16. Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
  17. Erin Illman and Paul Temple. 2019. California Consumer Privacy Act. The Business Lawyer 75, 1 (2019), 1637–1646.
  18. Approximate Data Deletion from Machine Learning Models. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 130), Arindam Banerjee and Kenji Fukumizu (Eds.). PMLR, 2008–2016.
  19. Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (2002), 422–446.
  20. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206.
  21. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
  22. Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation. arXiv:2203.11491 [cs.IR]
  23. Forgetting Fast in Recommender Systems. arXiv:2208.06875 [cs.IR]
  24. Contrastive Self-supervised Sequential Recommendation with Robust Augmentation. arXiv:2108.06479 [cs.IR]
  25. Learn to Forget: Machine Unlearning via Neuron Masking. IEEE Transactions on Dependable and Secure Computing 20, 4 (2023), 3194–3207. https://doi.org/10.1109/TDSC.2022.3194884
  26. Alessandro Mantelero. 2013. The EU Proposal for a General Data Protection Regulation and the roots of the ‘right to be forgotten’. Computer Law & Security Review 29, 3 (2013), 229–235.
  27. Factorizing Personalized Markov Chains for Next-Basket Recommendation (WWW ’10). Association for Computing Machinery.
  28. Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy. arXiv:2305.06360 [cs.LG]
  29. An MDP-Based Recommender System. Journal of Machine Learning Research 6, 43 (2005), 1265–1295.
  30. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP). IEEE, 3–18.
  31. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.
  32. Jiaxi Tang and Ke Wang. 2018. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. arXiv:1809.07426 [cs.IR]
  33. Machine Unlearning via Algorithmic Stability. arXiv:2102.13179 [cs.LG]
  34. Attention is all you need. Advances in neural information processing systems 30 (2017).
  35. GIF: A General Graph Unlearning Strategy via Influence Function. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW ’23). Association for Computing Machinery, New York, NY, USA, 651–661. https://doi.org/10.1145/3543507.3583521
  36. Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 33, 01 (Jul. 2019), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346
  37. Contrastive Learning for Sequential Recommendation. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). 1259–1273. https://doi.org/10.1109/ICDE53745.2022.00099
  38. Self-supervised reinforcement learning for recommender systems. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 931–940.
  39. Machine Unlearning: A Survey. ACM Comput. Surv. (jun 2023). https://doi.org/10.1145/3603620 Just Accepted.
  40. Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations. arXiv:2302.06676 [cs.LG]
  41. A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining.
  42. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). Association for Computing Machinery, New York, NY, USA, 1893–1902. https://doi.org/10.1145/3340531.3411954
  43. Membership Inference Attacks Against Sequential Recommender Systems. In Proceedings of the ACM Web Conference 2023 (WWW ’23). 1208–1219.
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Authors (7)
  1. Xin Xin (49 papers)
  2. Liu Yang (194 papers)
  3. Ziqi Zhao (29 papers)
  4. Pengjie Ren (95 papers)
  5. Zhumin Chen (78 papers)
  6. Jun Ma (347 papers)
  7. Zhaochun Ren (117 papers)
Citations (2)
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