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Overview 

Overview

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WWW#$!" -    1?Web 43'27* +)8,   DAY RESEARCH TRACK DAY RESEARCH TRACK DAY RESEARCH TRACK 5/15 Fairness, Credibility and Search 5/15 Efficiency and Scalability 5/16 Health on the Web 5/15 Network Algorithms 5/15 Search 5/16 Sarcasm, Sentiment, and Language 5/15 Recommendation 5/16 Networks, Opinions, and Perceptions 5/17 Graph Models 5/15 Security 5/16 Personalization! 5/17 Sarcasm, Sentiment, and Language 5/15 Knowledge Synthesis 5/16 Crowdsourcing and Human Computation 5/17 Economics, Monetization, and Online Markets 5/15 Knowledge Analysis and Querying 5/16 Text Classification and Relation Extraction 5/17 Social Recommendation and Experimentation 5/15 Communities, Complaints, and Collective Action 5/16 Systems and Infrastructure 5/17 Topic Modeling and Representation 5/15 Network Applications 5/16 Network and Analysis 5/15 Privacy and Trust 5/16 Mobile and Ubiquitous Computing Research Track/:0<&  = > (@  

Overview
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Day1-Day2 1. WorkshopInternational Working on Modeling Social Media 2. TutorialA/B Testing at Scale (Microsoft) 3. TutorialOnline User Engagement (Spotify)! 4. BIGRecommending and Searching, Research @ Spotify 5. TutorialPrivacy-preserving Data Mining in Industry (LinkedIn) Day3-Day5 • Keynote, Poster Session • Research Track   

    Sponsor Booth Day1-Day223 Workshop
22 Tutorial Day3-Day512 Research Track
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Keynote 

Tutorial 

Tutorial: Online User Engagement Slides
h(ps://onlineuserengagement.github.io/ Speaker: Mounia Lalmas Research Director & Head of Tech Research @Spo?fy 20HKUser Engagement Personaliza?on Track Chair BIG Track Plenary Speaker Topic 1.Metrics • '%'$

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https://onlineuserengagement.github.io/ Paolo Dragone, Rishabh Mehrotra and Mounia Lalmas. Deriving User- and Content-specific Rewards for Contextual Bandits. WWW 2019. Metrics
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Research Track Personaliza1on 


  Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges O; 5?=L`D 8T 3'(VA) Emotional IntelligenceRC Listening Between the Lines: Learning Personal Attributes from Conversations :h%!'MB
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Research Track - Personalization: Dynamic Ensemble of Contextual Bandits to Satisfy User’s Changing Interests 

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Tutorial • Online User Engagement: Metrics and Op4miza4on • h8ps://onlineuserengagement.github.io/ Reference Paper • Deriving User- and Content-specific Rewards for Contextual Bandits • h8ps://labtomarket.files.wordpress.com/2019/03/www2019_re wards.pdf • Dynamic Ensemble of Contextual Bandits to Sa4sfy Users' Changing Interests • h8p://www.cs.virginia.edu/~hw5x/paper/WWW2019- DenBandit-Wu.pdf • Exploring Perceived Emo4onal Intelligence of Personality-Driven Virtual Agents in Handling User Challenges • h8ps://dl.acm.org/cita4on.cfm?id=3313400 • Listening Between the Lines: Learning Personal A8ributes from Conversa4ons • h8ps://arxiv.org/abs/1904.10887 • Dual Neural Personalized Ranking • h8ps://dl.acm.org/cita4on.cfm?id=3313585 • Quality Effects on User Preferences and Behaviors in Mobile News Streaming • h8p://www.thuir.org/group/~YQLiu/publica4ons/WWW2019Lu.pdf 

Appendix 

Dynamic Ensemble of Contextual Bandits ) • b !" o #$ • b o / Ø admissibleFbandit expert d c Ø admissibleDF id BFexpert auditor m Expected Reward T . 1 1,2 ls t ( . T . 1 1,2 rDe B n ( . 2-, %&'$," = 1 +" $ , -∈/0 1 %&'$," (3) 5 3$," = arg 39:-∈/0 1 ( ̂ <$," 3 − ' $," > ) !" = arg 3!?$∈@ %&'$," ̂ <$," 3 ≤ lsvg a =Badness a ! 3B ! 3C " # $ % " $ # % & ⛹ ( ) & ⛹ ( ) " 3B admissible & admissibleDF expert Time Research Track - Personaliza>on: Dynamic Ensemble of Contextual Bandits to Sa>sfy User’s Changing Interests 

Research Track -Personaliza2on: Dynamic Ensemble of Contextual Bandits to Sa2sfy User’s Changing Interests Dynamic Ensemble of Bandit Experts ; DenBand
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