Application of recall methods in recommendation systems

Abstract

In order to have a more comprehensive introduction and understanding of the re-search progress of recall strategies in recommender systems, this paper reviews the application of diverse recall methods in various recommender systems by different researchers. By searching and reading literature in major databases like Google Scholar, it is found that the recall method suitable for news recommendation system is also generally applicable in other recommendation systems. Therefore, this paper takes news recommendation system as an example to introduce traditional content-based recall and collaborative filtering-based methods. Hot-based recall and Embed-ding-based recall also developed in recent years. Furthermore, recall strategies (emo-tion-based recall and UIBB) that are specifically applicable to music and e-commerce recommendation systems are introduced. This paper briefly introduces these recall styles and collects researchers' evaluations and attitudes towards these recall styles, aiming to provide help for recommender system designers in optimizing recall methods.