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Recommandé
准备了一个星期 的ppt,但是最后学院忽然忽然的通知不用准备ppt,很生气也很无奈,做了就不想要自己的东西流产,拿出来晒晒吧。
硕士毕业论文答辩ppt--LBS社区口碑营销
硕士毕业论文答辩ppt--LBS社区口碑营销
bebeyond2008
The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user plays a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recom- mender System) which will be the rst in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance as- pects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets. The paper was published in ACM RecSys 2016. The paper can be accessed from the url http://dl.acm.org/citation.cfm?id=2959187.
MAPS: A Multi Aspect Personalized POI Recommender System
MAPS: A Multi Aspect Personalized POI Recommender System
rameshraj
Recommender system introduction
Recommender system introduction
Liang Xiang
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows. Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
Recommender Systems
Recommender Systems
Federico Cargnelutti
Slides for my 4 hour tutorial on Recommender Systems at the 2014 Machine Learning School at CMU
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Xavier Amatriain
Overview of recommender system
Overview of recommender system
Overview of recommender system
Stanley Wang
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
Recommender systems
Recommender systems
Tamer Rezk
By Coen Stevens, Lead Recommendations Engineer at Wakoopa. Presented at http://recked.org
How to build a recommender system?
How to build a recommender system?
blueace
Recommandé
准备了一个星期 的ppt,但是最后学院忽然忽然的通知不用准备ppt,很生气也很无奈,做了就不想要自己的东西流产,拿出来晒晒吧。
硕士毕业论文答辩ppt--LBS社区口碑营销
硕士毕业论文答辩ppt--LBS社区口碑营销
bebeyond2008
The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user plays a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recom- mender System) which will be the rst in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance as- pects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets. The paper was published in ACM RecSys 2016. The paper can be accessed from the url http://dl.acm.org/citation.cfm?id=2959187.
MAPS: A Multi Aspect Personalized POI Recommender System
MAPS: A Multi Aspect Personalized POI Recommender System
rameshraj
Recommender system introduction
Recommender system introduction
Liang Xiang
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows. Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
Recommender Systems
Recommender Systems
Federico Cargnelutti
Slides for my 4 hour tutorial on Recommender Systems at the 2014 Machine Learning School at CMU
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Xavier Amatriain
Overview of recommender system
Overview of recommender system
Overview of recommender system
Stanley Wang
Recommender systems are software tools and techniques providing suggestions for items to be of interest to a user. Recommender systems have proved in recent years to be a valuable means of helping Web users by providing useful and effective recommendations or suggestions.
Recommender systems
Recommender systems
Tamer Rezk
By Coen Stevens, Lead Recommendations Engineer at Wakoopa. Presented at http://recked.org
How to build a recommender system?
How to build a recommender system?
blueace
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017
Balázs Hidasi
Deep Learning for Recommender Systems Tutorial slides presented at ACM RecSys 2017 in Como, Italy.
Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial
Alexandros Karatzoglou
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users. The recommendations may consist of: -> retail items (movies, books, etc.) or -> actions, such as following other users in a social network. It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommendation Systems Basics
Recommendation Systems Basics
Jarin Tasnim Khan
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Movie Recommender System Using Artificial Intelligence
Movie Recommender System Using Artificial Intelligence
Shrutika Oswal
Session from 2014 IRM Summit in Phoenix, Arizona. Introduction to OpenDJ by Matthias Tristl of ForgeRock
OpenDJ: An Introduction
OpenDJ: An Introduction
ForgeRock
Slides for the presentation on "Session-based recommender systems" at Qvik.
Session-Based Recommender Systems
Session-Based Recommender Systems
Eötvös Loránd University
Presented at Online ITIL Indonesia Webinar #5. Content: > Setting up the context > Understanding holistic IT Management point of view > IT Service Management Transformation > Key Performance Indicator (KPI) > IT Service Catalogue > IT Sourcing > Agile Incident Management
Modern IT Service Management Transformation - ITIL Indonesia
Modern IT Service Management Transformation - ITIL Indonesia
Eryk Budi Pratama
Diversity is a desirable property of recommendations. Diversity can be increased with the use of re-rankers. This work presents an alternative approach where diversity is optimised together with accuracy during a matrix factorisation learning.
Incorporating Diversity in a Learning to Rank Recommender System
Incorporating Diversity in a Learning to Rank Recommender System
Jacek Wasilewski
June 2018 talk on Architecting Recommender Systems by James Kirk at Spotify
Boston ML - Architecting Recommender Systems
Boston ML - Architecting Recommender Systems
James Kirk
Analysis and details of the various recommendation systems. Authored by: Arnaud De Bruyn
A Hybrid Recommendation system
A Hybrid Recommendation system
Pranav Prakash
Tutorial on "Evaluating Recommender Systems: Ensuring Reproducibility of Evaluation". With Alan Said.
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
Alejandro Bellogin
See what Juniper customers have to say about how Mist AI has statistically improved the way they run their business. For more information on how Mist AI can improve your network, join us for Transformation Thursdays at: https://www.juniper.net/us/en/forms/ai-driven-demo/
Real AI. Real Results. Mist AI Customer Testimonials.
Real AI. Real Results. Mist AI Customer Testimonials.
Juniper Networks
social relationships provides a new opportunity for improving the quality of recommender systems
Social Recommender Systems
Social Recommender Systems
guest77b0cd12
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
nextlib
Invited Talk at State University of New York at Buffalo EAS 504: Applications of Data Science – Industrial Overview - Guest Lectures
Role of Data Science in eCommerce
Role of Data Science in eCommerce
ManojKumar Rangasamy Kannadasan
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Recommendation Systems
Recommendation Systems
Robin Reni
淘宝的推荐系统实践经验分享
空望 推荐系统@淘宝
空望 推荐系统@淘宝
topgeek
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推荐系统规划
推荐系统规划
2005000613
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推荐与广告
agawu
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腾讯大讲堂15 市场研究及数据分析理念及方法概要介绍
腾讯大讲堂15 市场研究及数据分析理念及方法概要介绍
PMCamp
Sns与系统架构浅谈
Sns与系统架构浅谈
taobaby365
recommendation system
Recommendation system
Recommendation system
光明 赵
Contenu connexe
Tendances
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017
Balázs Hidasi
Deep Learning for Recommender Systems Tutorial slides presented at ACM RecSys 2017 in Como, Italy.
Deep Learning for Recommender Systems RecSys2017 Tutorial
Deep Learning for Recommender Systems RecSys2017 Tutorial
Alexandros Karatzoglou
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users. The recommendations may consist of: -> retail items (movies, books, etc.) or -> actions, such as following other users in a social network. It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommendation Systems Basics
Recommendation Systems Basics
Jarin Tasnim Khan
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Movie Recommender System Using Artificial Intelligence
Movie Recommender System Using Artificial Intelligence
Shrutika Oswal
Session from 2014 IRM Summit in Phoenix, Arizona. Introduction to OpenDJ by Matthias Tristl of ForgeRock
OpenDJ: An Introduction
OpenDJ: An Introduction
ForgeRock
Slides for the presentation on "Session-based recommender systems" at Qvik.
Session-Based Recommender Systems
Session-Based Recommender Systems
Eötvös Loránd University
Presented at Online ITIL Indonesia Webinar #5. Content: > Setting up the context > Understanding holistic IT Management point of view > IT Service Management Transformation > Key Performance Indicator (KPI) > IT Service Catalogue > IT Sourcing > Agile Incident Management
Modern IT Service Management Transformation - ITIL Indonesia
Modern IT Service Management Transformation - ITIL Indonesia
Eryk Budi Pratama
Diversity is a desirable property of recommendations. Diversity can be increased with the use of re-rankers. This work presents an alternative approach where diversity is optimised together with accuracy during a matrix factorisation learning.
Incorporating Diversity in a Learning to Rank Recommender System
Incorporating Diversity in a Learning to Rank Recommender System
Jacek Wasilewski
June 2018 talk on Architecting Recommender Systems by James Kirk at Spotify
Boston ML - Architecting Recommender Systems
Boston ML - Architecting Recommender Systems
James Kirk
Analysis and details of the various recommendation systems. Authored by: Arnaud De Bruyn
A Hybrid Recommendation system
A Hybrid Recommendation system
Pranav Prakash
Tutorial on "Evaluating Recommender Systems: Ensuring Reproducibility of Evaluation". With Alan Said.
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
Alejandro Bellogin
See what Juniper customers have to say about how Mist AI has statistically improved the way they run their business. For more information on how Mist AI can improve your network, join us for Transformation Thursdays at: https://www.juniper.net/us/en/forms/ai-driven-demo/
Real AI. Real Results. Mist AI Customer Testimonials.
Real AI. Real Results. Mist AI Customer Testimonials.
Juniper Networks
social relationships provides a new opportunity for improving the quality of recommender systems
Social Recommender Systems
Social Recommender Systems
guest77b0cd12
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
nextlib
Invited Talk at State University of New York at Buffalo EAS 504: Applications of Data Science – Industrial Overview - Guest Lectures
Role of Data Science in eCommerce
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ManojKumar Rangasamy Kannadasan
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Tendances
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Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017
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Recommendation Systems Basics
Movie Recommender System Using Artificial Intelligence
Movie Recommender System Using Artificial Intelligence
OpenDJ: An Introduction
OpenDJ: An Introduction
Session-Based Recommender Systems
Session-Based Recommender Systems
Modern IT Service Management Transformation - ITIL Indonesia
Modern IT Service Management Transformation - ITIL Indonesia
Incorporating Diversity in a Learning to Rank Recommender System
Incorporating Diversity in a Learning to Rank Recommender System
Boston ML - Architecting Recommender Systems
Boston ML - Architecting Recommender Systems
A Hybrid Recommendation system
A Hybrid Recommendation system
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
HT2014 Tutorial: Evaluating Recommender Systems - Ensuring Replicability of E...
Real AI. Real Results. Mist AI Customer Testimonials.
Real AI. Real Results. Mist AI Customer Testimonials.
Social Recommender Systems
Social Recommender Systems
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
Role of Data Science in eCommerce
Role of Data Science in eCommerce
Recommendation Systems
Recommendation Systems
Similaire à 动态推荐系统关键技术研究
淘宝的推荐系统实践经验分享
空望 推荐系统@淘宝
空望 推荐系统@淘宝
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推荐与广告
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腾讯大讲堂15 市场研究及数据分析理念及方法概要介绍
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PMCamp
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sallyke41
UX RESEARCH
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产品思考
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資訊科技策略方格-羅靖婷 陳盈穎 李怡叡 王朝川
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0407 contextual enquiry and usability test for massage chair
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动态推荐系统关键技术研究
1.
动态推荐系统关键技术研究 项亮 指导老师:杨青
研究员
2.
主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 2 Recommender System
3.
引言 推荐系统的主要任务 帮助用户发现他们可能感兴趣的内容(个性化推荐系统)
将内容投放给可能会对它们感兴趣的用户(个性化广告) 推荐系统无论在工业界还是学术界都是一个重要的研究热点。 3
4.
引言 著名商业推荐系统 4
5.
引言 推荐系统的主要算法 按照使用数据分:
协同过滤 :用户行为数据 内容过滤 : 用户内容属性和物品内容属性 社会化过滤:用户之间的社会网络关系 按照模型分: 最近邻模型:基于用户/物品的协同过滤算法 Latent Factor Model:基于矩阵分解的模型 图模型:二分图模型,社会网络图模型 5
6.
引言 推荐系统中常见的时间效应 用户兴趣的变化
物品流行度的变化 季节效应 6
7.
引言 协同过滤数据集: {(用户,物品,行为,时间)}
问题: 通过研究用户的历史行为和兴趣爱好,预测用户将来的行为和喜好。 是用户集合, 是物品集合, 是时间集合 7
8.
主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 8
9.
问题简述 数据集:显性反馈数据集 {(用户,物品,评分,时间)}
问题定义 给定用户u,物品i,时间t,预测用户u在时间t对物品i的评分 9
10.
相关研究 时间无关的评分预测问题算法 基于用户/物品的协同过滤算法
基于矩阵分解的模型 Latent Factor Model 受限波尔兹曼机 RBM 时间相关的评分预测问题算法 用户会喜欢和他们最近喜欢的物品相似的物品 用户会喜欢和他们兴趣相似的用户最近喜欢的物品 10
11.
时间效应 时间效应一:全局平均分的变化 Netflix数据集中用户评分平均分随时间的变化曲线
11
12.
时间效应 时间效应二:物品平均分的变化 Netflix数据集中物品平均分随物品在线时间的变化曲线
12
13.
时间效应 时间效应三:用户偏好的变化 13
14.
时间效应 时间效应四:用户兴趣的变化 用户对物品的兴趣会随时间发生改变。
年龄增长:青年->中年 生活状态变化:学生->工作 社会热点影响:北京奥运会 14
15.
时间效应 时间效应五:季节效应 15
16.
模型和算法 用户兴趣模型 时间无关的Latent
Factor Model (RSVD) 时间相关的Latent Factor Model (TRSVD) 3 5 1 5 3 2 2 4 4 2 3 4 5 1 2 16
17.
模型和算法 Tensor分解 17
物品 用 户 时间
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模型和算法 模型优化 18
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模型和算法 季节效应 19
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实验分析 数据集(Netflix数据集) 评测指标
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实验分析 实验结果 TRSVD和RSVD模型在Probe测试集上的RMSE比较
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实验分析 实验结果 季节效应的影响
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主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 23
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问题简述 数据集:隐性反馈数据集 {(用户,物品,时间)}
问题定义 给定用户u,时间t,预测用户u在时间t可能会喜欢的物品列表R(u) 24
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相关研究 基于邻域的协同过滤算法 ItemCF:推荐给用户那些和他们之前喜欢的物品类似的物品
UserCF:推荐给用户那些和他们兴趣相似的用户喜欢的物品 基于评分数据的Top-N推荐算法 推荐给用户那些他们可能评分最高的物品 25
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时间效应 用户兴趣分为短期兴趣和长期兴趣 短期兴趣:临时,易变
长期兴趣:长久,稳定 短期兴趣可能会转化为长期兴趣 26 因此,需要在推荐系统中综合考虑用户的长期兴趣和短期兴趣。
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两个顶点之间的路径比较短;
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两个顶点之间的路径不经过有很大出度的顶点。个性化推荐问题可以转变为计算用户节点和物品节点的相关性的问题。 c C
d D 27
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模型和算法 路径融合算法 找出用户顶点和物品顶点之间的最短路径;
计算每条最短路径的权重; 将所有最短路径的权重线性叠加作为最终用户对物品喜好程度的度量。 28
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模型和算法 用户时间段图模型 顶点权重定义
A a A:1 A:2 b B B:1 c 用户u对物品i的兴趣函数: B:2 29
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模型和算法 基于图的个性化推荐算法 30
P(A,c,2) A A A A A A a a a a a a A:1 A:1 A:1 A:1 A:1 A:1 A:2 A:2 A:2 A:2 A:2 A:2 b b b b b b B B B B B B B:1 B:1 B:1 B:1 B:1 B:1 c c c c c c B:2 B:2 B:2 B:2 B:2 B:2
33.
实验分析 数据集 CiteULike
: 4607个用户,16,054篇论文和109,364条用户和论文之间的关系记录 Delicious : 8,861个用户,3,257篇网页和59,694条用户和网页之间的收藏关系记录 评测指标 31
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实验分析 实验结果 CiteULike
Delicious 32
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实验分析 实验结果 CiteULike
Delicious 33
36.
主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 34
37.
问题简述 每个在线系统都是一个动态系统,但它们有不同的演化速率。 新闻,博客演化的很快,但音乐,电影的系统演化的却比较慢。
不同演化速率的系统需要不同类型的推荐算法。 Fast Slow 35
38.
在线系统的变化速率 这幅图显示了不同系统,相似热门度的物品的平均生存周期。 一个物品的生存周期定义为该物品被至少一个用户关注过的天数。
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在线系统的变化速率 这幅图显示了不同系统,相隔t天的两天,item热门程度的相似度。 图表显示,NYTimes的演化很快,相隔1天,item的热门程度就会有很大的变化。而对于Netflix,即使过了2个月,热门电影也没有太大的变化
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模型和算法 时间段图模型 38
a A A b A a B A:1 c a a:1 A:1 A:2 b A:2 b B b:1 B c B:1 c B:1 (A,a,1) (A,c,2) (B,b,1) (B,c,2) c:2 B:2 B:2
41.
模型和算法 时间段图模型 39
顶点权重定义 A a A:1 a:1 A:2 b B b:1 c B:1 用户u对物品i的兴趣函数: c:2 B:2
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实验分析 数据集 评测指标
Precision/Recall 40
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实验分析 实验结果 41
8种算法在5个数据集上的召回率(N = 20)
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时效性的影响 实验结果 42
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46.
主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 44
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动态推荐系统原型 推荐系统架构 45
用户行为数据库 用户界面 日志系统 推荐引擎
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用户行为数据库 行为提取和分析 用户行为模型
用户兴趣特征 相似度表 相关推荐 初步推荐结果 用户反馈模型 推荐解释 结果过滤和排名 最终推荐结果 离线系统 在线系统 动态推荐系统架构 46
49.
主要内容 引言 动态评分预测问题
动态Top-N推荐问题 时效性的影响 动态推荐系统原型 小结与展望 47
50.
小结与展望 小结 基于矩阵分解的动态用户兴趣模型
考虑用户长期兴趣和短期兴趣的动态用户兴趣模型 网站时效性对用户行为和推荐系统设计的影响 48
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小结与展望 展望 用户不同种类行为的动态模型
用户兴趣动态模型对推荐系统其他指标的影 推荐系统随时间的演化规律 49
52.
感谢杨老师的指导感谢各位评审老师 Q&A
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