Description: :简要介绍了电子商务推荐系统的概念、作用及组成构件,给出了推荐技术分类标准,系统综述了协同
过滤推荐、基于内容推荐、基于人口统计信息推荐、基于效用推荐、基于知识推荐和基于规则推荐等6 种主要的推
荐技术。-: Briefly introduce the concept of e-commerce recommendation system, the role and composition of components, given the recommended technical classification standards, the system recommended by an overview of collaborative filtering, content-based recommendation, based on demographic information recommendation, based on the effectiveness of the recommendation of the knowledge-based recommendation and 6 kinds of rule-based recommendation technology major recommendation. Platform: |
Size: 316416 |
Author:qinger |
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Description: The Duine Framework is a (collection of) software libraries that allows developers to create prediction engines for their own applications. A prediction engine is a component that predicts how interested individual users are in pieces of information. Such predictions can be used to personalise information to users, specifically in recommending to users what information is and is not of interest to them.
Duine is the Irish Gaelic word for person and is pronounced as “dinne” (diner without an ‘r’).
Duine has been developed by the Telematica Instituut/Novay and is based on scientific research on personalisation and specifically into recommender systems. The scientific research results on which the Duine software is based are available at the Telematica Instituut/Novay, you can download the pdf here Platform: |
Size: 11327488 |
Author:yan |
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Description: 本书以机器学习与计算统计为主题背景，专门讲述如何挖掘和分析Web上的数据和资源，如何分析用户体验、市场营销、个人品味等诸多信息，并得出有用的结论，通过复杂的算法来从Web网站获取、收集并分析用户的数据和反馈信息，以便创造新的用户价值和商业价值。全书内容翔实，包括协作过滤技术（实现关联产品推荐功能）、集群数据分析（在大规模数据集中发掘相似的数据子集）、搜索引擎核心技术（爬虫、索引、查询引擎、PageRank算法等）、搜索海量信息并进行分析统计得出结论的优化算法、贝叶斯过滤技术（垃圾邮件过滤、文本过滤）、用决策树技术实现预测和决策建模功能、社交网络的信息匹配技术、机器学习和人工智能应用等。-The book and calculation of statistical machine learning as the theme background, specifically on how to dig and analyze data on the Web and resources, how to analyze the user experience, marketing, personal tastes and many other information, and draw useful conclusions, through a complex algorithm from the Web site to access, collect and analyze user data and feedback information in order to create a new user value and business value. Informative book, including the collaborative filtering technology (to achieve related product recommendation function), the cluster data analysis (in large-scale data set to explore a subset of similar data), search engine core technology (reptiles, index, query engine, PageRank algorithm, etc.), Search for massive information and statistical analysis concluded that the optimization algorithms, Bayesian filtering technology (spam filtering, text filtering), with technology forecasting and decision tree modeling, social network information matching techn Platform: |
Size: 2633728 |
Author:陈磊 |
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Description: BM3D去噪算法的实现和相关文档，很好的去噪算法-Image and video denoising by sparse 3D transform-domain collaborative filtering
Block-matching and 3D filtering (BM3D) algorithm
Platform: |
Size: 2907136 |
Author:瓶子 |
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Description: Collaborative Filtering
CS294 Practical Machine Learning
Aleksandr Simma (asimma@cs)
Based on slides by Pat Flaherty Platform: |
Size: 467968 |
Author:vahid |
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Description: 本书以机器学习与计算统计为主题背景，专门讲述如何挖掘和分析Web上的数据和资源，如何分析用户体验、市场营销、个人品味等诸多信息，并得出有用的结论，通过复杂的算法来从Web网站获取、收集并分析用户的数据和反馈信息，以便创造新的用户价值和商业价值。全书内容翔实，包括协作过滤技术（实现关联产品推荐功能）、集群数据分析（在大规模数据集中发掘相似的数据子集）、搜索引擎核心技术（爬虫、索引、查询引擎、PageRank算法等）、搜索海量信息并进行分析统计得出结论的优化算法、贝叶斯过滤技术（垃圾邮件过滤、文本过滤）、用决策树技术实现预测和决策建模功能、社交网络的信息匹配技术、机器学习和人工智能应用等。 是Web开发者、架构师、应用工程师等的绝佳选择。-Book and calculation of statistical machine learning as the theme background, specifically on how to mine and analyze data and resources on the Web, how to analyze the user experience, marketing, personal tastes, and many other information, and draw useful conclusions, through complex algorithms Obtained from the Web site to collect and analyze data and user feedback in order to create new customer value and business value. Informative book, including collaborative filtering technology (associated with product recommendation capabilities to achieve), the cluster data analysis (in large-scale data set to explore a subset of similar data), the core search engine technology (crawler, index, query engine, PageRank algorithm, etc.) Massive search for statistical information and analysis concluded that the optimization algorithms, Bayesian filtering technology (spam filtering, text filtering), using decision tree technology for forecasting and decision modeling, social network information ma Platform: |
Size: 2630656 |
Author:也席 |
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Description: Apache Mahout 是 Apache Software Foundation（ASF）旗下的一个开源项目，提供一些可扩展的机器学习领域经典算法的实现，旨在帮助开发人员更加方便快捷地创建智能应用程序。经典算法包括聚类、分类、协同过滤、进化编程等-Apache Mahout is the Apache Software Foundation (ASF)' s an open source project, to provide some areas of expansion of the classic machine learning algorithm, designed to help developers more quickly and easily create intelligent applications. Classical algorithms such as clustering, classification, collaborative filtering, evolutionary programming, etc. Platform: |
Size: 5095424 |
Author:xingchao |
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Description: 简要介绍了电子商务推荐系统的概念、作用及组成构件,给出了推荐技术分类标准,系统综述了协同
过滤推荐、基于内容推荐、基于人口统计信息推荐、基于效用推荐、基于知识推荐和基于规则推荐等6 种主要的推
荐技术。对这些推荐技术的优缺点进行了比较,介绍了推荐评价技术。重点评述了电子商务个性化推荐领域中的
研究热点问题,并分析了目前国内电子商务个性化推荐理论研究和应用现状,最后提出了电子商务个性化推荐领
域所面临的挑战。-To make E- business system actively recommend product s to users according to their interest s , re2
search on E- business recommended systems was firstly described. Concept s , functions and constituent s of E-
business recommended system were briefly int roduced. The technical recommendation standard was given. Six
main recommend technologies such as collaborative filtering recommendation , recommendation based on con2
tent s , population statistics , efficiency , information and rules were mentioned. Advantages and disadvantages of
these above- mentioned technical recommendations were provided. Recommendation evaluation was also int ro2
duced. Hot topics in personalized E- business recommendation research were emphasized. Then , existing prob2
lems on personalized recommendation in China were analyzed. Future research challenges facing E- business
personalized recommendation were presented at last. Platform: |
Size: 316416 |
Author:ming |
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Description: data mining project consisting of three types of classification algos--content based,knowledge based and collaborative filtering Platform: |
Size: 1960960 |
Author:ankita |
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Description: 协同过滤，相似性算法，一篇很好的文章，很有参考价值-Collaborative filtering, similarity algorithm, a very good article, great reference value Platform: |
Size: 214016 |
Author:宋阳 |
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Search - collaborative filtering