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User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS).
On information and sufficiency
Kullback, S.; and Leibler, R. A. 1951 · 1951
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2012 · 2012
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Cao, Y.; and Yang, J. 2015 · 2015
Earlier work this paper cites.
Forget me now: Fast and exact unlearning in neighborhood-based recommendation
Schelter, S.; Ariannezhad, M.; and de Rijke, M. 2023 · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations
Covington, P.; Adams, J.; and Sargin, E. 2016 · 2016
Earlier work this paper cites.
Two decades of recommender systems at Amazon. com
Smith, B.; and Linden, G. 2017 · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Voigt, P.; and Von dem Bussche, A. 2017 · 2017
Earlier work this paper cites.
Spotify and Wixen settle the music publishing company’s $1.6 billion lawsuit
Deahl, D. 2018 · 2018
Earlier work this paper cites.
Poisoning attacks to graph-based recommender systems
Fang, M.; Yang, G.; Gong, N. Z.; and Liu, J. 2018 · 2018
Earlier work this paper cites.
Warner Music Group pulls music from YouTube
Levine, R. 2018 · 2018
Earlier work this paper cites.
Graphcar: Content-aware multimedia recommendation with graph autoencoder
Xu, Q.; Shen, F.; Liu, L.; and Shen, H. T. 2018 · 2018
Earlier work this paper cites.
MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video
Wei, Y.; Wang, X.; Nie, L.; He, X.; Hong, R.; and Chua, T.-S. 2019 · 2019
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A.; Achille, A.; and Soatto, S. 2020 · 2020
Earlier work this paper cites.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
He, X.; Deng, K.; Wang, X.; Li, Y.; Zhang, Y.; and Wang, M. 2020 · 2020
Earlier work this paper cites.
Revisiting adversarially learned injection attacks against recommender systems
Tang, J.; Wen, H.; and Wang, K. 2020 · 2020
Earlier work this paper cites.
Graph-refined convolutional network for multimedia recommendation with implicit feedback
Wei, Y.; Wang, X.; Nie, L.; He, X.; and Chua, T.-S. 2020 · 2020
Earlier work this paper cites.
Machine Unlearning
Bourtoule, L.; Chandrasekaran, V.; Choquette-Choo, C. A.; Jia, H.; Travers, A.; Zhang, B.; Lie, D.; and Papernot, N. 2021 · 2021
Earlier work this paper cites.
Amnesiac machine learning
Graves, L.; Nagisetty, V.; and Ganesh, V. 2021 · 2021
Earlier work this paper cites.
Mitigating sentiment bias for recommender systems
Lin, C.; Liu, X.; Xv, G.; and Li, H. 2021 · 2021
Cited alongside, same era.
Learn locally, correct globally: A distributed algorithm for training graph neural networks
Ramezani, M.; Cong, W.; Mahdavi, M.; Kandemir, M. T.; and Sivasubramaniam, A. 2021 · 2021
Cited alongside, same era.
Mining latent structures for multimedia recommendation
Zhang, J.; Zhu, Y.; Liu, Q.; Wu, S.; Wang, S.; and Wang, L. 2021 · 2021
Cited alongside, same era.
Recommendation unlearning
Chen, C.; Sun, F.; Zhang, M.; and Ding, B. 2022a · 2022
Cited alongside, same era.
Graph unlearning
Chen, M.; Zhang, Z.; Wang, T.; Backes, M.; Humbert, M.; and Zhang, Y. 2022b · 2022
Cited alongside, same era.
Unlearning protected user attributes in recommendations with adversarial training
Ganhör, C.; Penz, D.; Rekabsaz, N.; Lesota, O.; and Schedl, M. 2022 · 2022
Sequence Unlearning for Sequential Recommender Systems
Ye, S.; and Lu, J. 2023 · 2023
Later among the works it cites.
Multi-view graph convolutional network for multimedia recommendation
Yu, P.; Tan, Z.; Lu, G.; and Bao, B.-K. 2023 · 2023
Later among the works it cites.
Federated unlearning for on-device recommendation
Yuan, W.; Yin, H.; Wu, F.; Zhang, S.; He, T.; and Wang, H. 2023 · 2023
Later among the works it cites.
Mmrec: Simplifying multimodal recommendation
Zhou, X. 2023 · 2023
Later among the works it cites.
Bootstrap latent representations for multi-modal recommendation
Zhou, X.; Zhou, H.; Liu, Y.; Zeng, Z.; Miao, C.; Wang, P.; You, Y.; and Jiang, F. 2023b · 2023
Later among the works it cites.
A unified framework for continual learning and machine unlearning
Chatterjee, R.; Chundawat, V.; Tarun, A.; Mali, A.; and Mandal, M. 2024 · 2024
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Cited alongside, same era.
A critical assessment of the algorithmic accountability act of 2022
Gursoy, F.; Kennedy, R.; and Kakadiaris, I. 2022 · 2022
Cited alongside, same era.
Forgetting fast in recommender systems
Liu, W.; Wan, J.; Wang, X.; Zhang, W.; Zhang, D.; and Li, H. 2022 · 2022
Cited alongside, same era.
A survey of machine unlearning
Nguyen, T. T.; Huynh, T. T.; Nguyen, P. L.; Liew, A. W.-C.; Yin, H.; and Nguyen, Q. V. H. 2022 · 2022
Cited alongside, same era.
Federated Unlearning with Knowledge Distillation
Wu, C.; Zhu, S.; and Mitra, P. 2022 · 2022
Cited alongside, same era.
A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Wu, L.; He, X.; Wang, X.; Zhang, K.; and Wang, M. 2022 · 2022
Cited alongside, same era.
Netflix and Forget: Fast Severance From Memorizing Training Data in Recommendations
Xu, M.; Sun, J.; Yang, X.; Yao, Y.; and Wang, C. 2022 · 2022
Cited alongside, same era.
Closest in time.
Post-Training Attribute Unlearning in Recommender Systems
Chen, C.; Zhang, Y.; Li, Y.; Meng, D.; Wang, J.; Zheng, X.; and Yin, J. 2024 · 2024
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ConDa: Fast Federated Unlearning with Contribution Dampening
Chundawat, V. S.; Niroula, P.; Dhungana, P.; Schoepf, S.; Mandal, M.; and Brintrup, A. 2024 · 2024
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Bridging Language and Items for Retrieval and Recommendation
Hou, Y.; Li, J.; He, Z.; Yan, A.; Chen, X.; and McAuley, J. 2024 · 2024
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Will tiktok videos be muted? here’s what to know after licensing deal with Universal Music ends
Johnson, A. 2024 · 2024
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Making recommender systems forget: Learning and unlearning for erasable recommendation
Li, Y.; Chen, C.; Zheng, X.; Liu, J.; and Wang, J. 2024 · 2024
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Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
Liu, W.; Zheng, X.; Chen, C.; Xu, J.; Liao, X.; Wang, F.; Tan, Y.; and Ong, Y.-S. 2024 · 2024
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Sharma, A. S.; Sarkar, N.; Chundawat, V.; Mali, A. A.; and Mandal, M. 2024 · 2024
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UnStar: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs
Sinha, Y.; Mandal, M.; and Kankanhalli, M. 2024 · 2024
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Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
Wang, H.; Lin, J.; Chen, B.; Yang, Y.; Tang, R.; Zhang, W.; and Yu, Y. 2024 · 2024
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On the Effectiveness of Unlearning in Session-Based Recommendation
Xin, X.; Yang, L.; Zhao, Z.; Ren, P.; Chen, Z.; Ma, J.; and Ren, Z. 2024 · 2024
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RRL: Recommendation Reverse Learning
You, X.; Xu, J.; Zhang, M.; Gao, Z.; and Yang, M. 2024 · 2024
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