推荐系统研究  

Review of Research on Recommendation System

作　　者：张素智[1] 赵亚楠[1] 杨芮[1] ZHANG Suzhi;ZHAO Yanan;YANG Rui(School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Chin)

机构地区：[1]郑州轻工业学院计算机与通信工程学院,河南郑州450002

出　　处：《湖北民族学院学报（自然科学版）》2017年第1期1-6,共6页Journal of Hubei Minzu University(Natural Science Edition)

基　　金：国家自然科学基金项目(61201447)

摘　　要：随着信息产业的飞速发展,信息资源的规模呈现出几何级的扩增,如何在这海量的数据中找出对用户有帮助的信息成为急需解决的问题.推荐系统作为解决互联网信息过载的主要途径之一,虽然经过多年的研究已存在多种推荐算法,也建立出了更为智能推荐系统,但是仍存面临着许多的难题,推荐算法仍是研究的热点.介绍了当前主要的推荐算法,指出了存在的不足,并说明了将来可能的研究方向.With the rapid development of information industry,the scale of information resources presents a geometric expansion,how to find useful information in this huge amount of data becomes an urgent problem to be resolved.One of the main ways to solve the overloading of Internet information is recommendation.Although many recommendation algorithms have been developed over the years,more intelligent recommendation system has been built.However,there are still many problems,recommendation algorithm is still a hot topic.This paper introduces the main current recommendation algorithms,points out the shortcomings,and illustrates possible future research directions.

关 键 词：信息过载 推荐算法 深度学习 数据挖掘 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]