  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.
