A Comprehensive Review of Group Recommendation Methods Based on Deep Learning
Group recommendation has emerged as a highly active research topic in the fields of information retriev-al and data mining in recent years.Its objective is to select a group of items from a large candidate set that is likely to be of interest to a set of users.With the advancement of deep learning,numerous group recommendation meth-ods based on deep learning have been proposed.This paper provides a brief introduction to the background know-ledge of this problem.It reviews the methods of data acquisition and conducts a comprehensive review,systematic classification,and in-depth analysis of group recommendation algorithms based on deep learning.In addition,this paper outlines some group recommendation datasets and evaluation methods suitable for deep methods,and con-ducts comparative experimental analysis and discussion on various recommendation algorithms.Finally,the re-search challenges in this field were analyzed,and valuable future research directions were discussed.
Group recommendationrecommender system overviewdeep learninggroup representation learning
万方数据
维普
