情境大数据建模及其在用户行为预测中的应用

吴书; 刘强; 王亮

doi:10.11959/j.issn.2096-0271.2016071

您当前的位置：

首页 >

文章列表页 >

情境大数据建模及其在用户行为预测中的应用

研究 | 更新时间：2024-06-03

- 情境大数据建模及其在用户行为预测中的应用
- Modeling contextual big data for user behavior prediction
- 大数据 2016年2卷第6期页码：2016071
- 作者机构：
- 作者简介：
  
  [ "吴书（1982-），男，中国科学院自动化研究所助理研究员，主要研究方向为数据挖掘和信息检索。先后主持多项国家科研项目，在重要期刊和顶级会议发表论文40余篇。" ]
  [ "刘强（1990-），男，中国科学院自动化研究所博士生，主要研究方向为数据挖掘，在顶级会议发表论文多篇。" ]
  [ "王亮（1975-），男，中国科学院自动化研究所研究员，博士生导师，IAPR会士和IEEE高级会员，模式识别国家重点实验室副主任，主要研究方向为机器学习、模式识别和计算机视觉。先后主持多项国家科研项目。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61403390);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1435221)
- DOI：10.11959/j.issn.2096-0271.2016071
  中图分类号： TP391.4
- 网络首发：2016-11，
  
  纸质出版：2016-11-20
- 稿件说明：
移动端阅览
吴书, 刘强, 王亮. 情境大数据建模及其在用户行为预测中的应用[J]. 大数据, 2016,2(6):2016071.

Shu WU, Qiang LIU, Liang WANG. Modeling contextual big data for user behavior prediction[J]. Big data research, 2016, 2(6): 2016071.
吴书, 刘强, 王亮. 情境大数据建模及其在用户行为预测中的应用[J]. 大数据, 2016,2(6):2016071. DOI： 10.11959/j.issn.2096-0271.2016071.

Shu WU, Qiang LIU, Liang WANG. Modeling contextual big data for user behavior prediction[J]. Big data research, 2016, 2(6): 2016071. DOI： 10.11959/j.issn.2096-0271.2016071.

摘要

随着大数据时代的到来，信息系统收集了海量情境信息，如舆情信息、环境信息、经济信息等。这些情景大数据提供丰富的细节信息，更细致地刻画行为背景以辅助用户行为建模。阐述了两种使用表达学习策略建模一般化情境信息的框架，并针对情境大数据中最常见的时序情境建模问题，使用循环神经网络建模时序情境中的序列依赖关系。

Abstract

In the big data era

information system has to handle a mass of data of contextual information

such as public opinion

environment information and economic status.Embedded with abundant details of user behavior

contextual information plays a significant role in effectively shaping user character and elaborately modeling user behavior.Two frameworks to model general context information through representation learning and a recurrent model for sequential context scenarios were involved.

关键词

Keywords

references

LAZER D , KENNEDY R , KING G , et al . The parable of Google flu:traps in big data analysis [J ] . Science , 2014 , 343 ( 6176 ): 1203 - 1205 .

XIONG R , NICHOLAS E P , SHEN Y . Deep learning stock volatilities with google domestic trends [J ] . 2015 :1512.04916.

YIN H , CUI B , CHEN L , et al . A temporal context-aware model for user behavior modeling in social media systems [C ] // The 2014 ACM SIGMOD International Conference on Management of Data,June 22-27,2014 , Utah,USA . New York : ACM Press , 2014 : 1543 - 1554 .

LIU Q , WU S , WANG L . COT:contextual operating tensor for context-aware recommender systems [C ] // Twenty-Ninth Conference on Artificial Intelligence,January 25-30,2015 , Austin Texas,USA .[S.l.:s.n. ] 2015 : 203 - 209 .

WU S , LIU Q , WANG L , et al . Contextual operation for recommender systems [J ] . IEEE Transactions on Knowledge and Data Engineering , 2016 , 28 ( 8 ): 2000 - 2012 .

LIU Q , WU S , WANG L . Collaborative prediction for multi-entity interaction with hierarchical representation [C ] // The 24th ACM International on Conference on Information and Knowledge Management,October 18-23,2015 , Melbourne,Australia . New York : ACM Press , 2015 : 613 - 622 .

LIU Q , WU S , WANG L , et al . Predicting the next location:a recurrent model with spatial and temporal contexts [C ] // Thirtieth AAAI Conference on Artificial Intelligence,February 12-17,2016 , Phoenix,USA .[S.l.:s.n. ] 2016 .

YU F , LIU Q , WU S , et al . A dynamic recurrent model for next basket recommendation [C ] // The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval,July 17-21 , Pisa,Italy . New York : ACM Press , 2016 : 729 - 732 .

XIONG L , CHEN X , HUANG T K , et al . Temporal collaborative filtering with bayesian probabilistic tensor factorization [C ] // The SIAM International Conference on Data Mining,April 29-May 1 , Ohio,USA .[S.l.:s.n. ] , 2010 : 211 - 222 .

RENDLE S . Factorization machines with libfm [J ] . Acm Transactions on Intelligent Systems and Technology , 2012 , 3 ( 3 ): 57 - 78 .

SINGH A P , GORDON G J . Relational learning via collective matrix factorization [C ] // The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,August 24-27 , Las Vegas,USA . New York : ACM Press , 2008 : 650 - 658 .

RENDLE S , FREUDENTHALER C,SCHMIDT-THIEME L . Factorizing personalized markov chains for nextbasket recommendation [C ] // International Conference on World Wide Web,April 26-30,2010 , Raleigh,USA . New York : ACM Press , 2010 : 811 - 820 .

PENNINGTON J , SOCHER R , MANNING C D . Glove:global vectors for word representation [C ] // EMNLP,October 25-29,Doha,Qatar , Doha,Qatar .[S.l.:s.n. ] , 2014 （ 14 ）： 1532 - 1543 .

ZHANG Y , DAI H , XU C , et al . Sequential click prediction for sponsored search with recurrent neural networks [J ] . Computer Science , 2014 : 1369 - 1375 .

浏览量

1100

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

跨社交媒体网络大数据下的用户建模