Review of personalized recommendation research based on meta-learning
As a tool to alleviate"information overload",recommendation system provides personal-ized recommendation services for users to filter redundant information,and has been widely used in re-cent years.However,in actual recommendation scenarios,there are often issues such as cold start and difficulty in adaptively selecting different recommendation algorithms based on the actual environment.Meta-learning,which has the advantage of quickly learning new knowledge and skills from a small num-ber of training samples,is increasingly being applied in research related to recommendation systems.This paper discusses the main research on using meta-learning techniques to alleviate cold start problems and adaptive recommendation issues in recommendation systems.Firstly,it analyzes the relevant re-search progress made in meta-learning-based recommendations in these two areas.Then,it points out the challenges faced by existing meta-learning recommendation research,such as difficulty in adapting to complex task distributions,high computational costs,and a tendency to fall into local optima.Finally,it provides an outlook on some of the latest research directions in meta-learning for recommendation sys-tems.
万方数据
维普
