隨著網路的迅速發展，每日產生的數據量呈現指數級增長，模型從日益龐大的數據集中學習，其中可能包括未經授權的個人資訊。針對這一問題，"the right to be forgotten"的概念在《一般資料保護規則》（GDPR）和《加州消費者隱私保護法》（CCPA）中扮演了關鍵角色，賦予用戶決定其個人資訊使用方式的權利。因此，一個名為機器遺忘學習（Machine Unlearning）的研究領域誕生，其目的是在保護用戶權利的同時保持模型的可用性。
在本研究中，我們專注於推薦系統的遺忘學習（Recommendation Unlearning），並提出了一個嶄新的框架，名為推薦系統的對比遺忘學習（RCU）。我們將對比式學習融合到此框架中，旨在使模型在遺忘指定數據的同時，保留其餘數據的完整性。通過對模型隱藏層的深度推理，我們從中提取隱含資訊，並運用重新排序技術改進推薦結果，使其更好地契合用戶偏好，從而優化最終推薦效果。
我們進行了各種遺忘學習任務的實驗，並在不同比例的遺忘請求下評估了性能。結果顯示，我們的模型在面對大量遺忘請求時，不僅保持了更好的推薦準確性和穩定性，還確保了極高的時間效率。此外，我們進行了針對用戶的遺忘實驗以及針對商品的遺忘實驗，結果顯示RCU能夠滿足用戶和平台提供者的多樣需求，證明其在不同情境中的多功能性。

With the rapid development of the internet, the exponential growth of data has led to models learning from increasingly larger datasets, including unauthorized data. In response, concepts like "right to be forgotten" have been pivotal in GDPR and CCPA regulations, granting users the right to determine the use of their data. Consequently, a burgeoning topic known as Machine Unlearning has emerged, aiming to protect user rights while maintaining model effectiveness.
In this study, we focus on recommendation unlearning and propose a new framework called Recommendation Contrastive Unlearning (RCU). We integrate contrastive learning into our framework, aiming to enable the model to forget specified data while preserving the integrity of remaining data. By leveraging inference on the model's hidden layers, we extract implicit information from item nodes. Through re-ranking techniques, we aim to refine our recommendation results to better align with user preferences, thereby optimizing our final recommendations.
We conducts experiment various unlearning tasks and evaluated the performance under different proportions of unlearning requests. The results demonstrate that our model not only maintains better recommendation accuracy and robustness when faced with a massive influx of unlearning requests but also ensures excellent time efficiency. Furthermore, RCU accommodates diverse needs of users and platform providers, proving its versatility across different scenarios.

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