基于社交网络的推荐系统研究  

Research on Recommendation System Based on Social Network

作　　者：蔡崇超 许华虎[1] CAI Chong-chao;XU Hua-hu(School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China;College of Logistic and Information Engineering,Huzhou Vocational&Technical College,Huzhou 313000,China)

机构地区：[1]上海大学计算机工程与科学学院,上海200444 [2]湖州职业技术学院物流与信息工程学院,浙江湖州313000

出　　处：《软件导刊》2020年第1期46-49,共4页Software Guide

摘　　要：近年来,基于社交网络的推荐系统随着社交媒体和大数据的蓬勃发展,逐渐成为推荐系统重点研究方向。将社交网络用户社会化属性信息和评论内容与深度学习等技术结合,可有效解决传统推荐系统数据稀疏和冷启动等问题。首先回顾传统推荐系统常用方法,介绍社交网络推荐系统主要流程和基本框架,然后介绍最新相关研究方向和应用情况,最后对基于社交网络的推荐系统发展趋势进行分析与展望。In recent years,with the vigorous development of social media and big data,recommendation system based on social net⁃work has gradually become one of the key research directions of recommendation system.Combining the social attribute information and comment contents of users in social networks with in-depth learning technology can alleviate the problems of data sparsity and cold start in traditional recommendation systems to a certain extent.This paper first reviews the common methods of traditional recommenda⁃tion system,then introduces the main processes and basic framework of social network recommendation system,compares and intro⁃duces its recent research directions and applications,and finally analyses and prospects the development trend of social net⁃work-based recommendation system.

关 键 词：推荐系统 社交网络 深度学习 矩阵分解 协同过滤 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]