Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dependent hashing and achieve good retrieval performance. However, due to increasing demand for users' privacy and security, we often need to remove users' data information from Machine Learning (ML) models to satisfy specific privacy and security requirements. This need requires the ANN search algorithm to support fast online data deletion and insertion. Current learning-based hashing methods need retraining the hash function, which is prohibitable due to the vast time-cost of large-scale data. To address this problem, we propose a novel data-dependent hashing method named unfolded self-reconstruction locality-sensitive hashing (USR-LSH). Our USR-LSH unfolded the optimization update for instance-wise data reconstruction, which is better for preserving data information than data-independent LSH. Moreover, our USR-LSH supports fast online data deletion and insertion without retraining. To the best of our knowledge, we are the first to address the machine unlearning of retrieval problems. Empirically, we demonstrate that USR-LSH outperforms the state-of-the-art data-distribution-independent LSH in ANN tasks in terms of precision and recall. We also show that USR-LSH has significantly faster data deletion and insertion time than learning-based data-dependent hashing.

翻译：近似最近邻（ANN）搜索是搜索引擎、推荐系统等应用的核心组件。近年来的诸多研究聚焦于基于学习的数据分布相关哈希方法，这些方法取得了优异的检索性能。然而，随着用户隐私与安全需求的日益增长，我们常需从机器学习（ML）模型中移除用户数据信息，以满足特定的隐私和安全要求。这一需求要求ANN搜索算法支持快速在线数据删除与插入。当前基于学习的哈希方法需要重新训练哈希函数，但由于大规模数据带来的高昂时间成本，这在实际中不可行。为解决此问题，我们提出了一种新颖的数据相关哈希方法——展开式自重构局部敏感哈希（USR-LSH）。我们的USR-LSH通过展开优化更新过程实现实例级数据重构，相比数据无关的LSH能更好地保留数据信息。更重要的是，USR-LSH无需重新训练即可支持快速在线数据删除与插入。据我们所知，这是首次解决检索问题中的机器遗忘任务。实验表明，在ANN任务中，USR-LSH在精确率和召回率方面均优于最先进的数据分布无关LSH方法。我们还验证了，USR-LSH的数据删除与插入时间显著快于基于学习的数据相关哈希方法。