User Modeling in Big Data. Qiang Yang, Huawei Noah s Ark Lab and Hong Kong University of Science and Technology 杨 强, 华 为 诺 亚 方 舟 实 验 室, 香 港 科 大

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1 User Modeling in Big Data Qiang Yang, Huawei Noah s Ark Lab and Hong Kong University of Science and Technology 杨 强, 华 为 诺 亚 方 舟 实 验 室, 香 港 科 大

2 Who we are: Noah s Ark LAB Have you watched the movie 2012? The flood is coming, but this time it is the data flood. Once here, it will never recede. This Noah s Ark Lab will lead Huawei to tackle: Big Data Mining & Artificial Intelligence. Carrier Business Enterprise Business Consumer Business

3 Noah s Ark Lab Head: Yang Qiang Deputy Head: Zhang Baofeng Chief Scientist Li Hang Associate Director Fan Wei NLP and IR Li Hang Language and information retrieval DM and ML Fan Wei Stream Data Mining, Graph Data Mining, and Smart Care the limit NLP Data Mining Social, Mobile, & HCI Big Data and AI Theory How to save costs in telecom industry? How to facilitate human language understanding and processing? What are the next-generation intelligent devices? Social Media, Mobile Intelligence, HCI Dai Wenyuan Large-scale mobile user and social user modeling, human computer interaction, devices of the future Theory 理 论 Theoretical foundations of big data and learning; push How to expand to new businesses with Big Data? What are the foundations of Big Data and Learning? 2

4 User Modeling is Never Ending L =Levels of Intelligence, T= Tasks, D=Data Lcan be accuracy, coverage, etc.t=learning tasks: Tcan be image understanding, document classification, etc. Intelligence: (dl/dd) > 0 and (dl/dt) >0 Lifelong Machine Learning: 2 L TD 0

5 Social Media User Modeling Noah s Ark Lab

6 Weibo Information Agent Lifelong Information Assistant Built on Social Media Each user and community can have her own Weibo Agent Weibo Agent can understand user/community s information needs Features (Basic) Following People, Re Tweeting, Generating Simple Comments Features (advanced) Learn to generate Tweets based on Analysis of Trends in Weibo and Other Sources Video of Weibo Agent user1 user2 Model 1 Model 2 5

7 User Message Model: UMM Message Model: UMM Solve data sparseness: user interests as prior for corresponding tweets topics Solve topic diversity: each tweet has its own topic distribution 6

8 Topics Discovered

9 Collaborative Activity Classification (ACM KDD 2013) Define pseudo label on friends data and learn from friends data: 8 8

10 Intelligent Phones: LML on the Phone 9

11 Streaming Data on the User Side Accel Accelerometer in the phone Application What application is used Calllog Length of a call Number of calls GSM The GSM station the phone is connected to. (location) Visit via GPS Path and Places the user visited 10

12 Data Resource Geneva Lake ~ 200 Users, Nokia Phones Rich data Accelerometer Locations Phones Charging status,wifi,gsm Signals 11 11

13 Nokia MDC(Mobile Data Context) 2012 Open challenge Rank Paper 1 Interdependence and Predictability of Human Mobility and Social Interactions Manlio De Domenico, Antonio Lima, Mirco Musolesi. University of Birmingham, UK 2 "Generating storylines from sensor data" Jordan Frank*, Shie Mannor, Doina Precup*. *McGill University, Montreal, Canada; Technion, Haifa, Israel 3 "Visual analysis of social networks in space and time" Aidan Slingsby, Roger Beecham, Jo Wood. City University London, UK Dedicated task 1: Semantic place prediction Where are you? Dedicated task 2: Next place prediction Where will you go? Dedicated task 3: Demographic prediction Who are you? 12

14 Results Qiang Yang s Team: Champion in 1 st and 3 rd tasks,over ~200 teams world-wide 中 国 计 算 机 学 会 通 信 2012 年 第 8 期 你 的 手 机 知 道 你 去 哪 里 MIT 技 术 评 论 链 接 根 据 传 感 器 数 据 产 生 故 事 传 感 器 数 据 在 时 空 上 的 可 视 化 视 频 链 接 空 间 13 11:48 到 达 礼 堂,11:50 离 开 礼 堂 11:52 到 达 休 息 室,12:38 离 开 休 息 室 时 间

15 User Modeling in Social Media (Weibo) 12% Information Extraction & Analysis Date/time User profile Geo-location Activity description Understanding of human activities in the real world 20% Activities POIs 8% 40% 18% 10% Temporal distribution 12% Activity distribution Spatial distribution 14

16 Discovering Spammers in Social Networks Yin Zhu, Erheng Zhong, Nanthan N. Liu, and Qiang Yang, HKUST Xiao Wang and He Li Ren Ren Hong Kong University of Science and Technology (HKUST) Renren Inc., China 15

17 Spammers hiding in the social network HKUST Yin Zhu, AAAI'12 16

18 Many spammers/fake accounts On page 22: undesirable accounts, which represent user profiles that we determine are intended to be used for purposes that violate our terms of service, such as spamming. Source Link As of December 31, 2012,, and undesirable accounts may have represented approximately 0.9% of our worldwide MAUs (monthly active users). HKUST 17

19 Our Approach A binary classification problem Use the massive social activities of users A server side approach Has not been studied previously Use social relationships to improve accuracy Spammer normal user network structure 18

20 Data 1: Social activities message activity 2 apps message Activity: Every click Every app use Every message user HKUST Yin Zhu, AAAI'12 19

21 Data 2: Social relationship user 1 1 Undirected social relation user HKUST Yin Zhu, AAAI'12 20

22 A previous assumption on spammer network structure Normal users spammers NOT TRUE in Renren.com [Cao et al. NSDI 12. Sybilrank.] HKUST Yin Zhu, AAAI'12 21

23 Dataset RenRen Data: 30K Users Spammers labeled by Renren Immune system, User appeals and customer support 1680 activities in total. Top activities : Visit-Album (17:9%), Show-Visit-Bulletin (11:9%), Visit-Blog (9:9%), Share/Retweet (9:7%), and Friend-Apply (2:1%). Summary: Reduce false positives significantly (~7%), and at the same time catches more spammers (~3%) 22

24 User Modeling and Transfer Learning

25 When Features are different Heterogeneous: different feature spaces Source: Text Target: Images Apple Banana The apple is the pomaceous fruit of the apple tree, species Malus domestica in the rose family Rosaceae... Banana is the common name for a type of fruit and also the herbaceous plants of the genus Musa which produce this commonly eaten fruit... 24

26 Heterogeneous Transfer Learning High level optimization problem How to estimate distance between distributions in the latent space? How to solve the resultant optimization problem? Latent Representation Target images 25

27 One Pic =? Documents? Qiang Yang et al. ACL 2010 Accuracy # text docs 26

28 Recommendation Systems 27

29 Personalized Mobile App Recommendation All users download history, and mobile apps descriptions Users choices will be considered as feedback to update our prediction model Pre-processing and Feature Extraction Data for model training Feature-based Collaborative Filtering Trained Model Depending on the user, time, location, smartphone models, prediction what mobile apps the user will be interested in Recommendations generated For the user

30 Product Recommendation Task: predict missing links in a network Focus: bipartite graph of users and items 29

31 Personalized Mobile App Recommendation (ICML 2013) Our Technology Large Scale Personalized Recommendation State-of-the-art feature-based collaborative filtering method Identify different types of users (e.g. game players, business users, etc.) through large scale data mining Extract useful features that hint the interests and preferences of different users Some useful features: keywords of apps, time of the day, users previously downloaded apps apps factors i users u = x factors r ui = 1 if u installed i 0 otherwise 30 30

32 Three Generations of Recommendation Technology 31

33 Conclusions Big Data Social and Mobile User Model Data Transfer learning User Model Theory More user model more accurate, and more efficient 32