The rapid development of information technology has led to information overload.Recommendation is one of 他the most effective ways to solve the information overload.In recent years,the rapid development of deep learning has also led to the advancement of recommender systems,and various deep learning based recommendation algorithms have emerged one after another.However,due to the large number of candidate items and the dynamic evolving of user interests,deep learning based recommendation algorithms suffer from computational burden of online recommendation.Therefore,it is almost impossible for these algorithms to be deployed alone in practice.With the development of deep learning based recommendation,the item recallingtechniques(also called approximated search techniques) has also made significant progress.This paper first introduces the research progress of the item recalling techniques based on the nearest neighbor search,and then discusses the research progress of the item recallingtechniquesbased on the maximum inner product search from the perspectives of indexing,locality sensitive hash,learning to hash and vector quantization.