Authors
- Chaochao Chen*
- Yizhao Zhang*
- Lingjuan Lyu
- Yuyuan Li*
- Jiaming Zhang
- Li Zhang
- Biao Gong
- Chenggang Yan
* External authors
Venue
- NeurIPS 2024
Date
- 2024
CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
Chaochao Chen*
Yizhao Zhang*
Yuyuan Li*
Jiaming Zhang
Li Zhang
Biao Gong
Chenggang Yan
* External authors
NeurIPS 2024
2024
Abstract
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a potential solution to enable selective forgetting in models, particularly in recommender systems where historical data contains sensitive user information. Despite recent advances in recommendation unlearning, evaluating unlearning methods comprehensively remains challenging due to the absence of a unified evaluation framework and overlooked aspects of deeper influence, e.g., fairness. To address these gaps, we propose CURE4Rec, the first comprehensive benchmark for recommendation unlearning evaluation. CURE4Rec covers four aspects, i.e., unlearning Completeness, recommendation Utility, unleaRning efficiency, and recommendation fairnEss, under three data selection strategies, i.e., core data, edge data, and random data. Specifically, we consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels. We construct multiple datasets with CURE4Rec evaluation and conduct extensive experiments on existing recommendation unlearning methods. Our code is released at https://github.com/xiye7lai/CURE4Rec.
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