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多层次场景感知评分预测研究 预览
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作者 郭望 胡文心 +2 位作者 吴雯 贺樑 窦亮 《计算机应用与软件》 北大核心 2019年第8期145-154,共10页
近年来,评论在电商等网络平台中起着越来越重要的作用。充分利用评论信息,可以更好地理解用户兴趣和物品性质,提升推荐系统的性能。但是,现有的基于评论的推荐模型都只在“单词”层面或“评论”层面之一建模,且没有考虑交互场景对用户... 近年来,评论在电商等网络平台中起着越来越重要的作用。充分利用评论信息,可以更好地理解用户兴趣和物品性质,提升推荐系统的性能。但是,现有的基于评论的推荐模型都只在“单词”层面或“评论”层面之一建模,且没有考虑交互场景对用户兴趣和物品性质的影响。因此提出一个新模型SCRM(Scene Context-aware Rating Prediction at Muti-level),同时在两个层面层次化、细粒度地抽取相关特征;在“评论”层面加入了场景上下文信息,突出当前场景中起主要影响的因素。在来自Amazon的不同领域上的四个公开数据集上进行了实验,结果显示基于均方误差SCRM整体上显著地超过了最先进的方法,包括MF、DeepCoNN、D-ATT和NARRE。 展开更多
关键词 推荐 评分预测 基于评论的推荐 层次化建模
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基于用户画像的图书馆推荐服务初探 预览
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作者 李丹 高建忠 《图书馆》 CSSCI 北大核心 2019年第7期66-71,共6页
面对图书馆信息过载的现状,如何构建用户画像,基于读者入馆行为数据为读者提供个性化的推荐服务,是图书馆实践大数据应用需要解决的问题。本文试图在图书馆广泛的读者行为框架中定位用户与应用系统的互动需求,探讨可以更好地在读者与INN... 面对图书馆信息过载的现状,如何构建用户画像,基于读者入馆行为数据为读者提供个性化的推荐服务,是图书馆实践大数据应用需要解决的问题。本文试图在图书馆广泛的读者行为框架中定位用户与应用系统的互动需求,探讨可以更好地在读者与INNOPAC等应用系统的互动关系中发挥良好作用的推荐方式。 展开更多
关键词 用户画像 推荐 读者特征 推荐服务
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UP-TreeRec:Building Dynamic User Profiles Tree for News Recommendation 预览
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作者 Ming He Xiaofei Wu +1 位作者 Jiuling Zhang Ruihai Dong 《中国通信:英文版》 SCIE CSCD 2019年第4期219-233,共15页
Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users.The key problem of personalized news recommendation is to model users’interests and ... Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users.The key problem of personalized news recommendation is to model users’interests and track their changes.A common way to deal with the user modeling problem is to build user profiles from observed behavior.However,the majority of existing methods make static representations of user profiles and little research has focused on effective user modeling that could dynamically capture user interests in news topics.To address this problem,in this paper,we propose UP-TreeRec,a news recommendation framework based on a user profile tree(UP-Tree),which is a novel framework combining content-based and collaborative filtering techniques.First,by exploiting a novel topic model namely UILDA,we obtain the representation vectors for news content in a topic space as the fundamental bridge to associate user interests with news topics.Next,we design a decision tree with a dynamically changeable structure to construct a user interest profile from the user’s feedback.Furthermore,we present a clustering-based multidimensional similarity computation method to select the nearest neighbor of the UP-Tree efficiently.We also provide a Map-Reduce framework-based implementation that enables scaling our solution to real-world news recommendation problems.We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves accuracy and effectiveness in news recommendation. 展开更多
关键词 NEWS RECOMMENDATION user PROFILING CONTENT-BASED RECOMMENDATION collaborative FILTERING
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苏洵的仕宦之路与书信书写 预览
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作者 仲皓 《乐山师范学院学报》 2019年第10期5-11,共7页
苏洵科举不第转而通过举荐入仕,书信在苏洵向朝中要员求荐的过程中发挥了极大的政治功能。这些书信既是苏洵自我认知和政治抱负的剖白,也间接反映了仁宗朝后期的政治动向和用人需求。苏洵在蜀隐居的经历使他在求荐信中塑造了一个满腹韬... 苏洵科举不第转而通过举荐入仕,书信在苏洵向朝中要员求荐的过程中发挥了极大的政治功能。这些书信既是苏洵自我认知和政治抱负的剖白,也间接反映了仁宗朝后期的政治动向和用人需求。苏洵在蜀隐居的经历使他在求荐信中塑造了一个满腹韬略、为朝廷排忧解难但不囿于流俗的隐士形象,这种形象建构的背后实则是北宋士人渴望加入政治序列,“得君行道”的普遍理想。 展开更多
关键词 苏洵 书信书写 举荐制 由隐入仕
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2019年一季度中国涂料行业运行情况简析
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作者 王臻 张玮航 刘杰 《中国涂料》 CAS 2019年第5期1-4,共4页
主要从全国涂料产量、主营业务收入和利润总额等方面简析了2019年一季度中国涂料行业运行情况,分析了面对近年化工行业产业政策、环保政策、金融政策、地方政令等多方面的影响,一季度行业发展中面临的问题。同时对2019年二季度行业发展... 主要从全国涂料产量、主营业务收入和利润总额等方面简析了2019年一季度中国涂料行业运行情况,分析了面对近年化工行业产业政策、环保政策、金融政策、地方政令等多方面的影响,一季度行业发展中面临的问题。同时对2019年二季度行业发展形势进行了简单预测,并提出了合理化建议。 展开更多
关键词 涂料 一季度 发展 问题 建议 2019年
Benign Strategy for Recommended Location Service Based on Trajectory Data 预览
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作者 Jing Yang Peng Wang Jianpei Zhang 《国际计算机前沿大会会议论文集》 2019年第1期17-19,共3页
A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on ... A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information. 展开更多
关键词 Location services COLLABORATIVE FILTERING RECOMMENDATION TRAJECTORY SIMILARITY
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Interest-Forgetting Markov Model for Next-Basket Recommendation 预览
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作者 Jinghua Zhu Xinxing Ma +1 位作者 Chenbo Yue Chao Wang 《国际计算机前沿大会会议论文集》 2019年第1期29-31,共3页
Recommendation systems provide users with ranked items based on individual’s preferences. Two types of preferences are commonly used to generate ranking lists: long-term preferences which are relatively stable and sh... Recommendation systems provide users with ranked items based on individual’s preferences. Two types of preferences are commonly used to generate ranking lists: long-term preferences which are relatively stable and short-term preferences which are constantly changeable. But short-term preferences have an important real-time impact on individual’s current preferences. In order to predict personalized sequential patterns, the long-term user preferences and the short-term variations in preference need to be jointly considered for both personalization and sequential transitions. In this paper, a IFNR model is proposed to leverage long-term and short-term preferences for Next-Basket recommendation. In IFNR, similarity was used to represent long-term preferences. Personalized Markov model was exploited to mine short-term preferences based on individual’s behavior sequences. Personalized Markov transition matrix is generally very sparse, and thus it integrated Interest-Forgetting attribute, social trust relation and item similarity into personalized Markov model. Experimental results are on two real data sets, and show that this approach can improve the quality of recommendations compared with the existed methods. 展开更多
关键词 Markov Social trust Next-Basket RECOMMENDATION Interest-Forgetting
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各国生物类似药参照药管理比对研究及完善我国生物类似药参照药管理的建议
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作者 张薰文 沈梦娟 +1 位作者 马玉琴 张象麟 《现代药物与临床》 CAS 2019年第4期896-899,共4页
参照药是生物类似药研发的标杆,对参照药进行规范管理,有利于保证生物类似药的研发质量。通过对美欧日韩等国家以及WHO对于生物类似药参照药的相关要求进行对比研究,提炼管理要素;结合我国参照药的现状、设计问卷、开展调研和专家研讨;... 参照药是生物类似药研发的标杆,对参照药进行规范管理,有利于保证生物类似药的研发质量。通过对美欧日韩等国家以及WHO对于生物类似药参照药的相关要求进行对比研究,提炼管理要素;结合我国参照药的现状、设计问卷、开展调研和专家研讨;综合对比研究、调研和研讨,提出完善我国生物类似药参照药管理的建议及建议的考量。 展开更多
关键词 生物类似药 参照药 比对研究 问卷调研 建议
各国生物类似药说明书管理的比对研究及完善我国生物类似药说明书管理的建议
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作者 陈宇 王海彬 +1 位作者 余美 王俭 《现代药物与临床》 CAS 2019年第4期900-903,共4页
对生物类似药的说明书进行规范管理,对指导医生合理用药、保证用药安全至关重要。通过对美欧日加等国家及我国生物类似药说明书管理要求进行比对研究,提炼管理要素;结合我国说明书的现状、设计问卷、开展调研和专家研讨;综合对比研究、... 对生物类似药的说明书进行规范管理,对指导医生合理用药、保证用药安全至关重要。通过对美欧日加等国家及我国生物类似药说明书管理要求进行比对研究,提炼管理要素;结合我国说明书的现状、设计问卷、开展调研和专家研讨;综合对比研究、调研和研讨,提出完善我国生物类似药说明书管理要求的建议和建议的考量。 展开更多
关键词 生物类似药 说明书管理 比对研究 问卷调研 建议
基于图游走的并行协同过滤推荐算法 预览
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作者 顾军华 谢志坚 +2 位作者 武君艳 许馨匀 张素琪 《智能系统学报》 CSCD 北大核心 2019年第4期743-751,共9页
针对目前协同过滤推荐算法存在的数据稀疏性问题和可扩展性问题,本文进行了相关研究。针对稀疏性问题,在传统的皮尔逊相关相似度中引入交占比系数计算用户间直接相似度,该方法缓解了用户间共同评分项的占比问题;提出一种基于图游走的间... 针对目前协同过滤推荐算法存在的数据稀疏性问题和可扩展性问题,本文进行了相关研究。针对稀疏性问题,在传统的皮尔逊相关相似度中引入交占比系数计算用户间直接相似度,该方法缓解了用户间共同评分项的占比问题;提出一种基于图游走的间接相似度计算方法,该方法根据用户间的直接相似度建立用户网络图,在用户网络图上通过游走计算用户间的间接相似度,并进行推荐。在Spark平台上实现本文方法的并行化,缓解了数据规模增加带来的可扩展性问题。实验结果表明:本文提出的算法在不同数据集上均取得了良好效果,有效地提高了推荐准确度,并且在分布式环境下具有良好的可扩展性。 展开更多
关键词 协同过滤 推荐 用户网络图 游走 相似度 间接相似度 并行 Spark平台
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各国生物类似药上市后监测要求及非可比生物制品对比研究及建议
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作者 王曦 靳征 +2 位作者 朱永红 余美 张克坚 《现代药物与临床》 CAS 2019年第4期907-910,共4页
通过对美国、欧盟、日本、韩国等国家生物类似药上市后监测的管理要求进行比对研究以及对WHO非可比生物制品管理的研究,提炼生物类似药上市后监测和非可比生物制品的管理要素;结合我国现状、设计问卷、开展调研和专家研讨,综合对比研究... 通过对美国、欧盟、日本、韩国等国家生物类似药上市后监测的管理要求进行比对研究以及对WHO非可比生物制品管理的研究,提炼生物类似药上市后监测和非可比生物制品的管理要素;结合我国现状、设计问卷、开展调研和专家研讨,综合对比研究、调研和研讨,提出完善我国生物类似药上市后监测及非可比生物制品管理的建议和建议的考量。 展开更多
关键词 生物类似药 上市后监测 非可比生物制品 比对研究 问卷调研 建议
各国生物类似药命名原则的比对研究及完善我国生物类似药命名原则的建议
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作者 王冲 周耘 +1 位作者 杨建红 张象麟 《现代药物与临床》 CAS 2019年第4期911-915,共5页
对生物类似药名称进行规范管理不仅有利于医生处方和患者用药的准确性,更重要的是利于药品上市后不良反应的可追溯。对美国、欧洲、日本、韩国等国家或地区以及WHO生物类似药命名方式、命名技术要求进行比对研究,尽管各国生物类似药的... 对生物类似药名称进行规范管理不仅有利于医生处方和患者用药的准确性,更重要的是利于药品上市后不良反应的可追溯。对美国、欧洲、日本、韩国等国家或地区以及WHO生物类似药命名方式、命名技术要求进行比对研究,尽管各国生物类似药的命名方式有所不同,但'可区分'是各国共同遵循的原则。在对比研究的基础上,结合我国命名、处方管理相关要求、问卷调研和专家研讨,提出完善我国生物类似药命名原则的建议。 展开更多
关键词 生物类似药 命名原则 比对研究 问卷调研 建议
基于深度学习的商品推荐系统研究 预览
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作者 杜少波 《价值工程》 2019年第26期237-238,共2页
随着移动互联网和电子商务的快速发展,网上购物已经成为人们生活的一部分。商品推荐系统可以提升用户体验,同时增加商品销售量。深度学习技术更加精准的分析、计算用户曾经浏览或购买的商品,因此基于深度学习技术的商品推荐系统可以更... 随着移动互联网和电子商务的快速发展,网上购物已经成为人们生活的一部分。商品推荐系统可以提升用户体验,同时增加商品销售量。深度学习技术更加精准的分析、计算用户曾经浏览或购买的商品,因此基于深度学习技术的商品推荐系统可以更加精准的为用户提供服务。 展开更多
关键词 深度学习 推荐 神经网络
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Empirical Effects of Online Retailing Recommendations 预览
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作者 Hsiaoping Yeh Fenghung Kuo 《心理学研究:英文版》 2019年第2期81-90,共10页
Online recommendation solves the current information overload problem in the online retailing businesses. Given relevant products by adopting recommendation algorithms, online shoppers can save time on searching and b... Online recommendation solves the current information overload problem in the online retailing businesses. Given relevant products by adopting recommendation algorithms, online shoppers can save time on searching and browsing for contents that they are interested in. Hence, in the increasing interests of online retailers, an empirical study was conducted to light the effectiveness of different entitled recommendations reflect on online shoppers. Working with a simulated online shopping establishment, the findings provide online retailers important guidelines regarding online customers’ behaviors. 展开更多
关键词 RECOMMENDER system ONLINE RECOMMENDATION ONLINE SHOPPING CUSTOMER behavior
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Generative API usage code recommendation with parameter concretization
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作者 Chi CHEN Xin PENG +5 位作者 Jun SUN Zhenchang XING Xin WANG Yifan ZHAO Hairui ZHANG Wenyun ZHAO 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2019年第9期51-72,共22页
Many programming languages and development frameworks have extensive libraries(e.g., JDK and Android libraries) that ease the task of software engineering if used effectively. With numerous library classes and sometim... Many programming languages and development frameworks have extensive libraries(e.g., JDK and Android libraries) that ease the task of software engineering if used effectively. With numerous library classes and sometimes intricate API(application programming interface) usage constraints, programmers often have difficulty remembering the library APIs and/or using them correctly. This study addresses this problem by developing an engine called DeepAPIRec, which automatically recommends the API usage code.Compared to the existing proposals, our approach distinguishes itself in two ways. First, it is based on a tree-based long short-term memory(LSTM) neural network inspired by recent developments in the machinelearning community. A tree-based LSTM neural network allows us to model and reason about variable-length,preceding and succeeding code contexts, and to make precise predictions. Second, we apply data-flow analysis to generate concrete parameters for the API usage code, which not only allows us to generate complete code recommendations but also improves the accuracy of the learning results according to the tree-based LSTM neural network. Our approach has been implemented for supporting Java programs. Our experimental studies on the JDK library show that at statement-level recommendations, DeepAPIRec can achieve a top-1 accuracy of about 37% and a top-5 accuracy of about 64%, which are significantly better than the existing approaches. Our user study further confirms that DeepAPIRec can help developers to complete a segment of code faster and more accurately as compared to IntelliJ IDEA. 展开更多
关键词 CODE RECOMMENDATION API deep learning data DEPENDENCY PARAMETER concretization
黑龙江迎春林业局林地质量等级利用分析 预览
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作者 张德军 《林业勘查设计》 2019年第2期16-18,共3页
加强林地利用,是可持续发展、生态文明建设的要求。根据国家林地质量利用的总体规划,统筹协调林地保护与利用,针对迎春林业局落实资源林地质量等级,对提高林地质量等级的利用进行分析。
关键词 森林资源 管护建设 建议
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基于频繁项集的多用户数据流混合推荐仿真 预览
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作者 王培培 胡威威 孙丽娜 《计算机仿真》 北大核心 2019年第9期434-437,共4页
针对当前数据流推荐存在安全性差和精确度低的问题,提出基于频繁项集的电商多用户数据流混合推荐方法。将蚁群算法与属性相关分析融合,将蚁群收敛所至路径判断为存在安全隐患的路径,对路径中存在安全隐患的数据进行判断,确定最终异常值... 针对当前数据流推荐存在安全性差和精确度低的问题,提出基于频繁项集的电商多用户数据流混合推荐方法。将蚁群算法与属性相关分析融合,将蚁群收敛所至路径判断为存在安全隐患的路径,对路径中存在安全隐患的数据进行判断,确定最终异常值并去除。基于数据流安全分析,引入增量挖掘算法,采用次频繁项索引表更新频繁树,并利用压缩FP-树与矩阵技术对新频繁树的频繁模式进行挖掘。根据数据流频繁模式挖掘,将不同数据流分类至相应主题组。基于频繁项集计算多用户访问相似程度,同时找到用户最近邻平时访问数据。结合主题抽取、最近邻访问及多用户共同兴趣相似度计算,将最符合用户的数据流推荐给用户,实现用户数据流混合推荐。实验结果表明,上述方法推荐过程安全性能好,且推荐结果准确度高,是一种切实可行的数据推荐方法。 展开更多
关键词 频繁项集 多用户 数据流 推荐
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Users Intention for Continuous Usage of Mobile News Apps: the Roles of Quality, Switching Costs, and Personalization
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作者 Qiongwei Ye Yumei Luo +3 位作者 Guoqing Chen Xunhua Guo Qiang Wei Shuyan Tan 《系统科学与系统工程学报:英文版》 SCIE EI CSCD 2019年第1期91-109,共19页
Mobile news apps have emerged as a significant means for learning about latest news and trends. However, in light of numerous news apps and information overload, motivating users to adopt one app is a major concern fo... Mobile news apps have emerged as a significant means for learning about latest news and trends. However, in light of numerous news apps and information overload, motivating users to adopt one app is a major concern for both the industry and academia. Therefore, considering the attributes of mobile news and the debate on switching costs in the Internet context, based on the expectation-confirmation model (ECM), this study suggests that switching costs still exist and have a significant moderating effect on user satisfaction and continuous usage of mobile news apps. Furthermore, the different influences of information quality, system quality and service quality on continuance intention, user satisfaction and switching costs are discussed, showing that quality of information has a significant impact on users’ continuous usage of mobile news apps through increasing perceived usefulness, whereas personalized service quality have stronger effects through increasing user satisfaction and switching costs. 展开更多
关键词 Personalized recommendation switching COSTS mobile news APPS expectation-confirmation model
A Content-Based Recommendation Framework for Judicial Cases 预览
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作者 Zichen Guo Tieke He +2 位作者 Zemin Qin Zicong Xie Jia Liu 《国际计算机前沿大会会议论文集》 2019年第1期86-88,共3页
Under the background of the Judicial Reform of China, big data of judicial cases are widely used to solve the problem of judicial research. Similarity analysis of judicial cases is the basis of wisdom judicature. In v... Under the background of the Judicial Reform of China, big data of judicial cases are widely used to solve the problem of judicial research. Similarity analysis of judicial cases is the basis of wisdom judicature. In view of the necessity of getting rid of the ineffective information and extracting useful rules and conditions from the descriptive document, the analysis of Chinese judicial cases with a certain format is a big challenge. Hence, we propose a method that focuses on producing recommendations that are based on the content of judicial cases. Considering the particularity of Chinese language, we use “jieba” text segmentation to preprocess the cases. In view of the lack of labels of user interest and behavior, the proposed method considers the content information via adopting TF-IDF combined with LDA topic model, as opposed to the traditional methods such as CF (Collaborative Filtering Recommendations). Users are recommended to compute cosine similarity of cases in the same topic. In the experiments, we evaluate the performance of the proposed model on a given dataset of nearly 200,000 judicial cases. The experimental result reveals when the number of topics is around 80, the proposed method gets the best performance. 展开更多
关键词 RECOMMENDATION CONTENT-BASED LDA COSINE SIMILARITY
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Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering 预览
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作者 Xiaolan Xie Mengnan Qiu 《国际计算机前沿大会会议论文集》 2019年第1期98-100,共3页
Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In... Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms. 展开更多
关键词 COLLABORATION FILTERING RECOMMENDATION algorithm LATENT Factor Model Cluster validity index SPECTRAL CLUSTERING
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