  In an era dominated by information overload, effective recommender systems
are essential for managing the deluge of data across digital platforms.
Multi-stage cascade ranking systems are widely used in the industry, with
retrieval and ranking being two typical stages. Retrieval methods sift through
vast candidates to filter out irrelevant items, while ranking methods
prioritize these candidates to present the most relevant items to users. Unlike
studies focusing on the ranking stage, this survey explores the critical yet
often overlooked retrieval stage of recommender systems. To achieve precise and
efficient personalized retrieval, we summarize existing work in three key
areas: improving similarity computation between user and item, enhancing
indexing mechanisms for efficient retrieval, and optimizing training methods of
retrieval. We also provide a comprehensive set of benchmarking experiments on
three public datasets. Furthermore, we highlight current industrial
applications through a case study on retrieval practices at a specific company,
covering the entire retrieval process and online serving, along with practical
implications and challenges. By detailing the retrieval stage, which is
fundamental for effective recommendation, this survey aims to bridge the
existing knowledge gap and serve as a cornerstone for researchers interested in
optimizing this critical component of cascade recommender systems.
