计算机科学与探索2024，Vol.18Issue(5)：1197-1210,14.DOI:10.3778/j.issn.1673-9418.2308044
推荐系统冷启动问题解决方法研究综述
Survey on Solving Cold Start Problem in Recommendation Systems
摘要
Abstract
Recommender systems provide important functions in areas such as dealing with data overload,providing personalized consulting services,and assisting clients in investment decisions.However,the cold start problem in recommender systems has always been in urgent need of solution and optimization.Based on this,this paper classi-fies the traditional methods and cutting-edge methods to solve the cold start problem,and expounds the research progress and excellent methods in recent years.Firstly,three traditional solutions to the cold start problem are sum-marized:recommendation based on content filtering,recommendation based on collaborative filtering,and hybrid recommendation.Secondly,the current cutting-edge recommendation algorithms to solve the cold start problem are summarized,and they are classified into the data-driven strategy and the method-driven strategy.The method-driven strategy is divided into algorithms based on meta-learning,algorithms based on context information and session str-ategy,algorithms based on random walk,algorithms based on heterogeneous graph information and attribute graph,and algorithms based on adversarial mechanism.According to the type of cold start problem,the algorithms are di-vided into two categories:new users and new items.Then,according to the particularity of the recommendation field,the cold start problem of the recommendation in the multimedia information field and the online e-commerce platform field is expounded.Finally,the possible research directions to solve the cold start problem in the future are summarized.关键词
冷启动/推荐系统/元学习/上下文信息/随机游走Key words
cold start/recommender systems/meta-learning/context information/random walk分类
信息技术与安全科学引用本文复制引用
毛骞,谢维成,乔逸天,黄小龙,董刚..推荐系统冷启动问题解决方法研究综述[J].计算机科学与探索,2024,18(5):1197-1210,14.基金项目
四川省科技成果转移转化项目(2020ZHCG0099) （2020ZHCG0099）
教育部春晖计划项目(Z2018087). This work was supported by the Transfer and Transformation of Scientific and Technological Project of Sichuan Province(2020ZHCG0099),and the Chunhui Project of the Ministry of Education of China(Z2018087). （Z2018087）
