Schedule

Saturday, Jan 31
8:45AM–9:00AM
Shang Yuan 500		 Winter School Opening Ceremony
9:00AM–12:00PM
Shang Yuan 500		 Winter School

  • Deep Learning for Web Searh and Natural Language Processing, Jianfeng Gao (Microsoft Research)
12:00PM–2:00PM Lunch
2:00PM–5:00PM
Shang Yuan 500		 Winter School

  • Natural Language Processing: Algorithms and Applications, Old and New, Noah Smith (Carnegie Mellon University)
Sunday, Feb 1
9:00AM–12:00PM
Shang Yuan 500		 Winter School

  • Fundamentals of Large Scale Social and Information Network, Jure Leskovec (Stanford University)
12:00PM–2:00PM Lunch
2:00PM–5:00PM
Shang Yuan 500		 Winter School

  • Distributed Systems and Algorithms for Scalable Machine Learning, Eric Xing (Carnegie Mellon University)
Monday, Feb 2
9:00AM–10:30AM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)		 Tutorials

  • Dynamic Information Retrieval Modeling, Hui Yang, Marc Sloan, and Jun Wang
    Room: Meeting Room 1+2 (2F)
  • Scalability and Efficiency Challenges in Large-Scale Web Search Engines, B. Barla Cambazoglu and Ricardo Baeza-Yates
    Room: Meeting Room 3 (2F)
  • Offline Evaluation and Optimization for Interactive Systems: A Practical Guide, Lihong Li
    Room: Terrace Room 1 (3F)
9:00AM–10:30AM
Terrace Room 2 (3F)		 Doctoral Consortium
  • Welcome and introduction
  • Student presentations (30 min each)
10:30AM–11:00AM
Foyer		 Coffee Break
11:00AM–12:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)		 Tutorials

  • Dynamic Information Retrieval Modeling, Grace Hui Yang, Marc Sloan, and Jun Wang
    Room: Meeting Room 1+2 (2F)
  • Scalability and Efficiency Challenges in Large-Scale Web Search Engines, B. Barla Cambazoglu and Ricardo Baeza-Yates
    Room: Meeting Room 3 (2F)
  • Offline Evaluation and Optimization for Interactive Systems: A Practical Guide, Lihong Li
    Room: Terrace Room 1 (3F)
11:00AM–12:30PM
Terrace Room 2 (3F)		 Doctoral Consortium
  • Student presentations (30 min each)
12:30PM–2:00PM Lunch
2:00PM–3:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)		 Tutorials

  • Real-Time Bidding: A New Frontier of Computational Advertising Research by Jun Wang and Shuai Yuan
    Room: Meeting Room 1+2 (2F)
  • Learning about health and medicine from Internet data by Elad Yom-Tov, Ingemar Johansson Cox and Vasileios Lampos
    Room: Meeting Room 3 (2F)
  • Distributed Graph Algorithmics: Theory and Practice by Silvio Lattanzi and Vahab Mirrokni
    Room: Terrace Room 1 (3F)
2:00PM–3:30PM
Terrace Room 2 (3F)		 Doctoral Consortium
  • 1-1 meetings with mentors (45 minutes for each mentor-student pair)
3:30PM–4:00PM
Foyer		 Coffee Break
4:00PM–5:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)		 Tutorials

  • Real-Time Bidding: A New Frontier of Computational Advertising Research by Jun Wang and Shuai Yuan
    Room: Meeting Room 1+2 (2F)
  • Learning about health and medicine from Internet data by Elad Yom-Tov, Ingemar Johansson Cox and Vasileios Lampos
    Room: Meeting Room 3 (2F)
  • Distributed Graph Algorithmics: Theory and Practice by Silvio Lattanzi and Vahab Mirrokni
    Room: Terrace Room 1 (3F)
4:00PM–5:30PM
Terrace Room 2 (3F)		 Doctoral Consortium
  • Panel discussions on research and career for PhD students
  • Wrap up
6:30PM–8:00PM
Mezzanine Bar (2F)		 Reception
Tuesday, Feb 3
8:45AM–9:00AM
Grand Ballroom (2F)		 Opening Session
9:00AM–10:00AM
Grand Ballroom (2F)
Chair: Xueqi Cheng		 Keynote Speech

  • Making Sense of Big Data with the Berkeley Data Analytics Stack, Michael Franklin (UC Berkeley)
10:00AM–10:30AM
Foyer		 Coffee Break
10:30AM–11:10AM
Grand Ballroom (2F)
Chair: Ying Li		 Session 1: Practice and Experience Talk

  • New Directions in Recommender Systems, Jure Leskovec (Stanford)
11:10AM–12:10PM
Grand Ballroom (2F)
Chair: Andrei Broder		 Session 1: Panel Discussion

  • Big Data: New Paradigm or “Sound and Fury, Signifying Nothing”?
12:10PM–1:30PM
Café Mix (1F)		 Lunch
1:30PM–3:10PM
Grand Ballroom (2F)
Chair: Maarten de Rijke		 Session 2: Web Search

  • Delayed-Dynamic-Selective (DDS) Prediction for Reducing Extreme Tail Latency in Web Search (L)
  • MergeRUCB: A Method for Large-Scale Online Ranker Evaluation (L)
  • Engagement Periodicity in Search Engine Usage: Analysis and its Application to Search Quality Evaluation (L)
  • Toward Predicting the Outcome of an A/B Experiment for Search Relevance (S)
  • Optimal Space-time Tradeoffs for Inverted Indexes (S)
  • Understanding and Predicting Graded Search Satisfaction (S)
  • Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis (S)
3:10PM–3:40PM
Foyer		 Coffee Break
3:40PM–5:20PM
Grand Ballroom (2F)
Chair: Elad Yom-Tov		 Session 3: Social Networks

  • The Power of Random Neighbors in Social Networks (L)
  • Negative Link Prediction in Social Media (L)
  • Sarcasm Detection on Twitter: A Behavioral Modeling Approach (L)
  • Modeling and Predicting Retweeting Dynamics on Microblogging Platforms (L)
  • On Integrating Network and Community Discovery (S)
  • On the Accuracy of Hyper-local Geotagging of Social Media Content (S)
6:00PM–8:00PM
Grand Ballroom (2F)		 Poster Session
Wednesday, Feb 4
9:00AM–10:00AM
Grand Ballroom (2F)
Chair: Evgeniy Gabrilovich		 Keynote Speech

  • The Information Life of Social Networks, Lada Adamic (Facebook)
10:00AM–10:30AM
Foyer		 Coffee Break
10:30AM–12:10PM
Grand Ballroom (2F)
Chair: Huan Liu		 Session 4: Web Mining

  • Learning to Recommend Related Entities to Search Users (L)
  • Will This Paper Increase Your h-Index? Scientific Impact Prediction (L)
  • Concept Graph Learning from Educational Data (L)
  • Review Synthesis for Micro-Review Summarization (S)
  • Fast and Space-Efficient Entity Linking in Queries (S)
  • On Tag Recommendation for Expertise Profiling: A Case Study in the Scientific Domain (S)
  • FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering (S)
12:10PM–1:30PM
Café Mix (1F)		 Lunch
1:30PM–2:50PM
Grand Ballroom (2F)
Chair: Paul Bennett		 Session 5: Practice and Experience Talk

  • Semantic Matching in APP Search, Juchao Zhuo (Tencent)
  • Boosting Search with Deep Understanding of Contents and Users, Kaihua Zhu (Baidu)
2:50PM–3:20PM
Foyer		 Coffee Break
3:20PM–5:00PM
Grand Ballroom (2F)
Chair: Charlie Clarke		 Session 6: Crowdsourcing, Temporal and Location-based mining

  • Driven by Food: Modeling Geographic Choice (L)
  • Hiring Behavior Models for Online Labor Markets (L)
  • Just in Time Recommendations – Modeling the Dynamics of Boredom in Activity Streams (L)
  • Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning (L)
  • Listwise Approach for Rank Aggregation in Crowdsourcing (S)
  • WorkerRank: Using Employer Implicit Judgements to Infer Worker Reputation (S)
6:00PM–8:00PM
Sea Palace Floating Restaurant		 Banquet
Thursday, Feb 5
9:00AM–10:00AM
Grand Ballroom (2F)
Chair: Jie Tang		 Keynote Speech

  • Learning from User Interactions, Thorsten Joachims (Cornell University)
10:00AM–10:30AM
Foyer		 Coffee Break
10:30AM–12:10PM
Grand Ballroom (2F)
Chair: Grace Hui Yang		 Session 7: User Modeling, Mobility, and Recommendation

  • User Modeling for a Personal Assistant (L)
  • Predicting the Next App that You Are Going to Use (L)
  • You Are Where You Go: Inferring Demographic Attributes from Location Check-Ins (L)
  • SimApp: A Framework for Detecting Similar Mobile Applications by Online Kernel Learning (L)
  • Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference (S)
  • Inferring Movement Trajectories from GPS Snippets (S)
12:10PM–1:30PM
Grand Ballroom (2F)		 WSDM Business Lunch Meeting
1:30PM–2:50PM
Grand Ballroom (2F)
Chair: Xuanjing Huang 		Session 8: Practice and Experience Talk

  • Regressing Towards Simpler Prediction Systems, Tushar Chandra (Google)
  • Global Optimization for Display Ad, Rong Jin (Alibaba)
2:50PM–3:20PM
Foyer		 Coffee Break
3:20PM–5:00PM
Grand Ballroom (2F)
Chair: Fabrizio Silvestri		 Session 9: Web Mining (2)

  • Back to the Past: Supporting Interpretations of Forgotten Stories by Time-aware Re-Contextualization (L)
  • Diluted Treatment Effect Estimation for Trigger Analysis in Online Controlled Experiments (L)
  • Inverting a Steady-State (L)
  • Automatic Gloss Finding for a Knowledge Base Using Ontological Constraints (S)
  • Finding Subgraphs with Maximum Total Density and Limited Overlap (S)
  • Modeling Website Popularity Competition in the Attention-Activity Marketplace (S)
  • Exploring the Space of Topic Coherence Measures (S)
5:05PM–5:20PM
Grand Ballroom (2F)		 Closing Session
Friday, Feb 6
9:00AM–10:30AM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)
Terrace Room 2 (3F) 		Workshops

  • HIA’15: Heterogeneous Information Access Workshop at WSDM 2015
    Room: Meeting Room 1+2 (2F)
  • DL-WSDM’15: Workshop on Deep Learning for Web Search and Data Mining
    Room: Meeting Room 3 (2F)
  • The 2nd Workshop on Vertical Search Relevance at WSDM 2015
    Room: Terrace Room 1 (3F)
  • WSDM’15 Workshop Summary / Scalable Data Analytics: Theory and Applications
    Room: Terrace Room 2 (3F)
10:30AM–11:00AM
Foyer		 Coffee Break
11:00AM–12:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)
Terrace Room 1 (3F)
Terrace Room 2 (3F)		 Workshops

  • HIA’15: Heterogeneous Information Access Workshop at WSDM 2015
    Room: Meeting Room 1+2 (2F)
  • DL-WSDM’15: Workshop on Deep Learning for Web Search and Data Mining
    Room: Meeting Room 3 (2F)
  • The 2nd Workshop on Vertical Search Relevance at WSDM 2015
    Room: Terrace Room 1 (3F)
  • WSDM’15 Workshop Summary / Scalable Data Analytics: Theory and Applications
    Room: Terrace Room 2 (3F)
12:30PM–2:00PM Lunch
2:00PM–3:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)		 Workshops

  • HIA’15: Heterogeneous Information Access Workshop at WSDM 2015
    Room: Meeting Room 1+2 (2F)
  • DL-WSDM’15: Workshop on Deep Learning for Web Search and Data Mining
    Room: Meeting Room 3 (2F)
3:30PM–4:00PM
Foyer		 Coffee Break
4:00PM–5:30PM
Meeting Room 1+2 (2F)
Meeting Room 3 (2F)		 Workshops

  • HIA’15: Heterogeneous Information Access Workshop at WSDM 2015
    Room: Meeting Room 1+2 (2F)
  • DL-WSDM’15: Workshop on Deep Learning for Web Search and Data Mining
    Room: Meeting Room 3 (2F)

Note: long presentations are labeled with (L) each has 20 minutes, while short presentations are labeled with (S) each has 10 minutes.

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