用户画像是真实用户的虚拟代表,是 建立在一系列真实数据(Marketing data,Usability data)之上的目标用户模型。通过用户调研去了解用户,根据他们的目标、行为和观点的差 异,将他们区分为不同的类型,然后每种类型中抽取出典型特征,赋予名字、照片、一些人口统计学要素、场景等描述,就形成了一个人物原型 。

知识荟萃

用户画像——专知荟萃

基础入门

  1. 架构师特刊:用户画像实践 by infoq
  2. luckydogzzy 用户画像学习日记
  3. 用户画像从入门到挖坑 by xrzs
  4. 浅谈用户画像在电商领域的现状和发展
  5. 永洪BI:手把手教您搞定用户画像
  6. 基于大数据的用户画像构建(理论篇)by 简书
  7. 知乎问题:什么是用户画像呢?一般用户画像的作用是什么? by
  8. 关于用户画像那些事,看这一文章就够了
  9. 看完后,别再说自己不懂用户画像了
  10. 用户画像,找到为你产品买单的那群人
  1. 内部课程|巧用“用户画像”进行个性化运营
  1. 【干货】浅谈“用户画像”方法

进阶文章

  1. 深度学习在用户画像标签模型中的应用
  2. 腾讯防刷负责人:基于用户画像大数据的电商防刷架构
  3. 用户画像系统实践 by 1号店精准化部架构师
  4. 外卖O2O的用户画像实践 by 美团点评技术团队
  5. 数据驱动精准化营销在大众点评的实践 by 美团点评技术团队
  6. 基于内容和用户画像的个性化推荐
  7. 基于知识图谱的用户理解 肖仰华 复旦大学
  8. 基于知识图谱的用户画像关键技术 肖仰华 复旦大学
  9. 大数据背后的360度用户画像,助力11.11新零售

竞赛

  1. 2016CCF 大数据精准营销中搜狗用户画像挖掘 代码
  2. SMP 2016 技术评测
  3. SMP 2017 CSDN用户画像技术评测

Papers

  1. App2Vec: Vector Modeling of Mobile Apps and Applications
  1. Personalizing search via automated analysis of interests and activities
    J Teevan, ST Dumais, E Horvitz - … of the 28th annual international ACM …, 2005
    https://dl.acm.org/citation.cfm?doid=1076034.1076111
  2. Automatic identification of user interest for personalized search 2006
    https://dl.acm.org/citation.cfm?id=1135883
  3. Implicit user modeling for personalized search X Shen, B Tan, CX Zhai  CIKM 2005
    https://dl.acm.org/citation.cfm?id=1099747
  4. User profiles for personalized information access S Gauch, M Speretta, A Chandramouli, A Micarelli 2007
    https://link.springer.com/chapter/10.1007%2F978-3-540-72079-9_2?LI=true
  5. Interest-based personalized search Z Ma, G Pant, ORL Sheng - ACM Transactions on Information Systems …, 2007 https://dl.acm.org/citation.cfm?id=1198301
  6. Mining long-term search history to improve search accuracy B Tan, X Shen, CX Zhai  KDD 2006
    https://dl.acm.org/citation.cfm?id=1150493
  7. Potential for personalization J Teevan, ST Dumais, E Horvitz 2010
    https://www.researchgate.net/publication/220286342_Potential_for_Personalization
  8. Towards TV recommender system: experiments with user modeling M Bjelica - IEEE Transactions on Consumer Electronics, 2010
    https://www.researchgate.net/publication/224184101_Towards_TV_Recommender_System_Experiments_with_User_Modeling
  9. Modeling user posting behavior on social media Z Xu, Y Zhang, Y Wu, Q Yang SIGIR 2012
    https://dl.acm.org/citation.cfm?id=2348358
  10. Extracting multilayered Communities of Interest from semantic user profiles: Application to group modeling and hybrid recommendations I Cantador, P Castells  2011
    https://dl.acm.org/citation.cfm?id=1982988
  11. U-sem: Semantic enrichment, user modeling and mining of usage data on the social web F Abel, I Celik, C Hauff, L Hollink 2011
    https://arxiv.org/abs/1104.0126v1
  12. Weakly Supervised User Profile Extraction from Twitter. 2014
    http://www.stanford.edu/~jiweil/ppt/attribute.pdf
  13. Harvesting multiple sources for user profile learning: a big data study
    A Farseev, L Nie, M Akbari, TS Chua 2015
    https://dl.acm.org/citation.cfm?id=2749381
  14. Improving user profile with personality traits predicted from social media content R Gao, B Hao, S Bai, L Li, A Li, T Zhu 2013
    https://dl.acm.org/citation.cfm?id=2507219

视频教程

  1. 电商大数据应用之用户画像, 慕课网
  1. 专访阿里交互数据师:如何通过数据挖掘用户画像
  1. 腾讯高级产品经理:如何做好用户画像、用户研究、竞品分析?
  1. 用户画像、性格分析与聊天机器人 by 微软亚洲研究院研究员 谢幸

PPT

  1. 用户画像的构建及应用 BY 百分点
  2. 基于Spark的实时用户画像分析系统-汪飞-1027
  3. 【分享31页PPT】基于用户画像的大数据挖掘实践
  4. 【业界实战】小米大数据总监司马云瑞详解小米用户画像的演进及应用解读(附报告pdf下载)

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题目: 面向智慧教育的学生认知建模与学习路径推荐

摘要: 如何自动建模和跟踪学生知识点掌握水平,是提升智慧教育中自适应学习能力的一个重要基础。报告将介绍从大规模异构学习数据中对学生进行认知诊断和知识跟踪的机器学习模型,以及基于学习者认知结构的自适应学习路径推荐方法。

个人简介: 陈恩红,中国科技大学教授,博导,国家杰出青年基金获得者,IEEE 高级会员(Senior Member)。2005年入选教育部新世纪优秀人才支持计划。现任中国科学技术大学计算机科学与技术学院副院长,语音及语言信息处理国家工程实验室副主任。教育部计算机类专业教学指导委员会委员,中国计算机学会理事、中国人工智能学会理事,中国计算机学会人工智能与模式识别专委会委员、数据库专委会委员、大数据专家委员会委员,中国人工智能学会知识工程与分布智能专业委员会副主任委员、机器学习专委会委员。

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Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large body of research on generating recommendations based on modeling user-item interaction sequences, few of them attempt to represent and transfer these models for serving downstream tasks where only limited data exists. In this paper, we delve on the task of effectively learning a single user representation that can be applied to a diversity of tasks, from cross-domain recommendations to user profile predictions. Fine-tuning a large pre-trained network and adapting it to downstream tasks is an effective way to solve such tasks. However, fine-tuning is parameter inefficient considering that an entire model needs to be re-trained for every new task. To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks. Specifically, PeterRec allows the pre-trained parameters to remain unaltered during fine-tuning by injecting a series of re-learned neural networks, which are small but as expressive as learning the entire network. We perform extensive experimental ablation to show the effectiveness of the learned user representation in five downstream tasks. Moreover, we show that PeterRec performs efficient transfer learning in multiple domains, where it achieves comparable or sometimes better performance relative to fine-tuning the entire model parameters. Codes and datasets are available at https://github.com/fajieyuan/sigir2020_peterrec.

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