推荐系统综述  

A survey on recommender systems

作　　者：赵岩[1] 刘宏伟[1] ZHAO Yan;LIU Hongwei(School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China)

机构地区：[1]哈尔滨工业大学计算机科学与技术学院,哈尔滨150001

出　　处：《智能计算机与应用》2021年第7期228-232,F0003,共6页Intelligent Computer and Applications

摘　　要：推荐系统是信息过滤系统领域的一个重要研究方向。随着信息技术的发展,推荐系统在提升用户体验和增加企业效益等方面发挥着越来越重要的作用。主流的推荐系统大多基于矩阵分解模型和深度学习模型,近年来又提出了基于记忆网络和集成学习的推荐系统为用户精确地推荐物品。本文将对基于矩阵分解、基于深度学习、基于记忆网络和基于集成学习的推荐系统进行分析和总结,展望未来的研究方向。Recommender systems are an important research direction in the field of information filtering systems.With the development of information technology,recommender systems play an increasingly important role in improving user experience and increasing enterprise revenue.Most of the mainstream recommendation systems are based on matrix factorization models and deep learning models.In recent years,researchers have proposed recommender systems based on memory networks and ensemble learning to accurately recommend items for users.This paper summarizes and introduces matrix factorization based,deep learning based,memory network based,and ensemble learning-based recommender systems,and discusses future research directions.

关 键 词：推荐系统 矩阵分解 神经网络 记忆网络 集成学习 

分 类 号：TP393.01[自动化与计算机技术—计算机应用技术]