Jiafeng Guo

CAS Key Lab of Web Data Science and Technology

Institute of Computing Technology

Top Conference Review:WSDM 2016

About WSDM

WSDM (pronounced "wisdom") is one of the premier conferences on web inspired

research involving search and data mining.

WSDM publishes original, high quality papers and presentations, with an emphasis on

practical but principled novel models of search, retrieval and data mining, algorithm

design and analysis, economic implications, and in-depth experimental analysis of

accuracy and performance.

About WSDM2016

Conference venue: Mission Bay Conference Center @ San Francisco, USA

Conference Co-Chairs:

Paul N. Bennett, Microsoft Research

Vanja Josifovski, Pinterest

368 submissions, 67 accepted (18.2% acceptance rate).

All accepted papers will appear in the proceedings as full-length publications and

will also be presented as both a talk, and a poster during an interactive session.

Overview

Schedule:

3 keynotes

4 invited talks for Practice & Experience Track

paper presentations

5 workshops

3 tutorials

a doctoral consortium

a VC/Industry Day

WSDM CUP

To provide the best static rank values for each of publication entity in a

heterogeneous graph (Microsoft Academic Graph)

Papers

Best Paper:

Beyond Ranking: Optimizing Whole-Page Presentation

Yue Wang, Dawei Yin, Roger Jie Luo, Pengyuan Wang, Makoto Yamada, Yi Chang

and Qiaozhu Mei

Honorable Mentions

Information Evolution in Social Networks

Lada A. Adamic, Thomas M. Lento, Eytan Adar and Pauline C. Ng

DiFacto: Distributed Factorization Machines

Mu Li, Ziqi Liu, Alexander Smola and Yu-Xiang Wang

Keynote @ Industry Day

Keynote by Ron Conway (Founder & Co-managing Partner, SV Angel)

Driving Innovation with Data Science

“A truly great founder is product focused & determined”

Keynote by Qi Lu (Executive Vice President, Microsoft)

The Anatomy of an Emerging Digital Society: A Look into the

Future from an Industrial Development Perspective

The 1st Era: PC and Client/Server

The 2nd Era: Internet and Web

The 3rd Era: Mobile and Cloud

“Conversation is the platform.” Natural language is the

ultimate UI.

Panels @ Industry Day

Panel 1: The role of data science in startups

Panel 2: How to transform business with data science

Panel 3: Data Science and Machine Learning in the Sharing Economy

Keynote @ First Day

Keynote by Jeff Dean (Google Senior Fellow, Google Research)

Large-Scale Deep Learning For Building Intelligent

Computer Systems

Google Brain

TensorFlow

Invited Talk @ First Day

Talk by Yoelle Maarek (Vice-President of Research, Yahoo)

Is Mail The Next Frontier In Search And Data Mining?

Email ranking by considering “actions”

Automated labels/Left categories across many users

Predicting future emails based on sequences

Keynote @ Second Day

Keynote by Yiling Chen (Gordon McKay Professor of Computer

Science, Harvard University)

Why Incentive Alignment is Relevant for Data Science

“what data become available depends on the interaction rules”

“jointly considers data acquisition and inference and learning

is important for data science and machine learning”

Invited Talks @ Second Day

Talk by Jie Tang (Associate Professor, Tsinghua University)

AMiner: Toward Understanding Big Scholar Data

Academic data mining

Talk by Lars Backstrom (Director of Engineering on News

Feed Facebook)

Serving a Billion Personalized News Feeds

Personalized feed ranking

Trending 1

It is critical to understand Users

Understanding User Attention and Engagement in Online News Reading (Social

Media)

Modeling and Predicting Learning Behavior in MOOCs (MOOC)

Your Cart tells You: Inferring Demographic Attributes from Purchase Data (Retail)

You've got Mail, and Here is What you Could do With It!: Analyzing and

Predicting Actions on Email Messages (Email)

Many papers discuss about how to model user behaviors, predict users’actions,

capture users’ preferences, enhance user engagement, and provide

personalized ranking/recommendations.

Trending 2

From Small to Big

Scaling up Link Prediction with Ensembles

DiFacto: Distributed Factorization Machines

Distributed Balanced Partitioning via Linear Embedding

Multi-view Machines

Multileave Gradient Descent for Fast Online Learning to Rank

A focused session on Big Data Algorithms, talking about large scale distributed

models and online learning algorithms.

Some feeling & thoughts

Very diverse topics in accepted papers

Mobile, search , recommendation, crowdsourcing,

privacy…

Deep learning is not dominant, but representation learning is

arising

Social computing is very popular

Social media, social network, communities, MOOC…

Mining, search, quality control, behavior prediction…

Thanks！