To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued embeddings. However, the embedding tables are huge and account for most of the parameters in industrial-scale recommender systems. In order to reduce memory costs and improve efficiency, various approaches are proposed to compress the embedding tables. In this survey, we provide a comprehensive review of embedding compression approaches in recommender systems. We first introduce deep learning recommendation models and the basic concept of embedding compression in recommender systems. Subsequently, we systematically organize existing approaches into three categories, namely low-precision, mixed-dimension, and weight-sharing, respectively. Lastly, we summarize the survey with some general suggestions and provide future prospects for this field.

翻译：为缓解信息爆炸问题，推荐系统被广泛部署以提供个性化信息过滤服务。通常，推荐系统采用嵌入表将高维稀疏的独热向量转换为稠密的实值嵌入。然而，嵌入表规模庞大，占据了工业级推荐系统中绝大部分参数。为降低内存成本并提升效率，学界提出了多种压缩嵌入表的方法。本综述系统梳理了推荐系统中的嵌入压缩技术。我们首先介绍了深度学习推荐模型及嵌入压缩的基本概念，随后将现有方法系统归纳为低精度、混合维度和权重共享三大类别。最后，我们总结出若干通用建议，并对该领域的未来发展方向进行展望。