With the explosive growth of users and items, Recommender Systems are facing unprecedented challenges in terms of retrieval efficiency and storage overhead. Learning to Hash techniques have emerged as a promising solution to these issues by encoding high-dimensional data into compact hash codes. As a result, hashing-based recommendation methods (HashRec) have garnered growing attention for enabling large-scale and efficient recommendation services. This survey provides a comprehensive overview of state-of-the-art HashRec algorithms. Specifically, we begin by introducing the common two-tower architecture used in the recall stage and by detailing two predominant hash search strategies. Then, we categorize existing works into a three-tier taxonomy based on: (i) learning objectives, (ii) optimization strategies, and (iii) recommendation scenarios. Additionally, we summarize widely adopted evaluation metrics for assessing both the effectiveness and efficiency of HashRec algorithms. Finally, we discuss current limitations in the field and outline promising directions for future research. We index these HashRec methods at the repository \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.

翻译：随着用户和物品数量的爆炸式增长，推荐系统在检索效率和存储开销方面正面临着前所未有的挑战。哈希学习技术通过将高维数据编码为紧凑的哈希码，已成为解决这些问题的有效方案。因此，基于哈希的推荐方法因其能够实现大规模、高效的推荐服务而受到越来越多的关注。本综述对当前先进的基于哈希的推荐算法进行了全面概述。具体而言，我们首先介绍了召回阶段常用的双塔架构，并详细阐述了两种主流的哈希搜索策略。接着，我们依据以下三个维度对现有工作进行了三层分类：(i) 学习目标，(ii) 优化策略，以及 (iii) 推荐场景。此外，我们总结了广泛采用的评估指标，用于衡量基于哈希的推荐算法的有效性和效率。最后，我们讨论了该领域当前的局限性，并展望了未来有前景的研究方向。我们将这些基于哈希的推荐方法索引在仓库 \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}。