基于系统动力学的资讯个性化推荐研究  

Study on information personalized recommendation based on system dynamics

作　　者：王子岩 司亮 刘滨[2,3] 刘宇[4] 孙中贤 刘增杰 张红斌[5] 刘青 WANG Ziyan;SI Liang;LIU Bin;LIU Yu;SUN Zhongxian;LIU Zengjie;ZHANG Hongbin;LIU Qing(School of Law,Hebei University of Economics and Business,Shijiazhuang,Hebei 050061,China;School of Economics and Management,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;Big Data and Social Computing Research Center,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;Library,Hebei Professional College of Political Science and Law,Shijiazhuang,Hebei 050061,China;School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China)

机构地区：[1]河北经贸大学法学院,河北石家庄050061 [2]河北科技大学经济管理学院,河北石家庄050018 [3]河北科技大学大数据与社会计算研究中心,河北石家庄050018 [4]河北政法职业学院图书馆,河北石家庄050061 [5]河北科技大学信息科学与工程学院,河北石家庄050018

出　　处：《河北科技大学学报》2021年第2期171-179,共9页Journal of Hebei University of Science and Technology

基　　金：河北省省级科技计划资助项目(20310802D,20310701D,18210109D);河北省高层次人才资助项目(A2016002015);河北省创新能力提升计划项目(20551801K);石家庄市科学技术研究与发展计划项目(19SCX01006,191130591A)

摘　　要：手机等移动智能终端在全社会的普及,使得数字内容的生产能力下沉到社会各个层面,形成了多源、自主、原生的互联网媒体内容制造格局;而以社交媒体、自媒体为代表的各类新兴媒体的蓬勃发展,使得数字内容的传播能力极大增强,大量衍生内容在敏感、热点、重要事件的报道传播中产生。互联网资讯具有海量、内容质量参差不齐、观点多极等特点。如何将价值导向对正确的、信息披露准确的资讯进行精准推荐,维护和促进社会公平正义,成为司法领域的新问题和新挑战。推荐系统有效解决了用户在海量信息中难以高效获得信息的问题。基于内容的推荐技术通过分析用户以往感兴趣的项目,经计算得到相似的项目,再将相似度最高的若干项目推送给用户。推荐系统中应用最广泛的是协同过滤推荐算法(collaborative filtering,CF),该概念最早于1992年由GOLDBERG等在开发Tapestry邮件过滤系统时首次提出,其核心思想是运用算法对用户的历史行为数据进行分析,挖掘出用户的兴趣偏好,根据不同的兴趣偏好对用户进行类别划分并推荐相似偏好的物品。当前,个性化推荐已经在电子商务、影视作品、餐饮美食、新闻资讯等领域获得了较为广泛的应用。"京东"的推荐起步于2012年,当时的产品推荐是基于规则匹配进行的,整个推荐产品线组合就像一个个松散的原始部落,部落与部落之间没有任何工程、算法的交集。"淘宝"从2013年推出了"个性化推荐"即"千人千面"的推荐引擎,利用用户的一些行为,通过算法推测出用户可能喜欢的东西。"美团"构建了世界上最大的菜品知识库,为200多万商家、3亿多件商品绘制了知识图谱,并为2.5亿用户画像,构建了世界上用户规模最大的O2O智能推荐平台。"豆瓣"利用社交行为分析解决推荐问题,如基于用户相同行为的协同过滤技术、友邻推荐等,�With the popularity of mobile intelligent terminals such as mobile phones in the whole society,the production capacity of digital content has sunk to all levels of the society,forming a multi-source,independent and native Internet media content manufacturing pattern.With the vigorous development of various emerging media represented by social media and we media,the propagation ability of digital content has been greatly enhanced,especially in the reporting of sensitive,hot and important events in the process of propagation,which will produce a lot of derivative content.The improvement of the above two abilities causes the internet information to be characterized by mass,uneven content quality and multi-point of view.How to accurately recommend the news of correct value orientation and accurate information disclosure related to judicial work to maintain and promote social fairness and justice has become a new problem and challenge in the judicial field.Recommender system effectively solved the problem that it was difficult for users to find the information they need efficiently in the mass of information.Content based recommendation technology analyzed the items that users are interested in before,got the similar items by calculation,and then pushed the items with the highest similarity to users.Collaborative filtering(CF)is the most widely used recommendation system,which was first proposed by Goldberg in 1992 when developing tapestry e-mail filtering system.Its core idea is to analyze the user′s historical behavior data through the algorithm,mine the user′s interest preferences,classify users according to different interest preferences,and recommend items with similar preferences.Collaborative filtering is the most widely used algorithm in recommendation system.It was first proposed by Goldberg in 1992 when developing tapestry e-mail filtering system.Its core idea was to analyze the user′s historical behavior data through the algorithm,mine the user′s interest preferences,classify users according to diff

关 键 词：数据处理 个性化推荐 司法工作 系统动力学 资讯平台 仿真模拟 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]