Xiangnan_KDD_Debrief

KDD’14 Debrief 24th April - 27stAugust, 2014 New York City, US

WING Monthly Meeting (Oct 24, 2014) Presented by Xiangnan He

Open Ceremony

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Welcome Words “Donot spend your precious time asking ‘Why isn’t the world a better place?’ It will only be time wasted. The question to ask is

‘How can I make it better?’ To that there is an answer.“ --- Leo Buscaglia

Overview • The largest KDD conference ever.     

Number of attendees: 2200 + (last year is 1176). 151 Research papers (20% growth over KDD’13), a 43 industry & govt. papers (30% growth) 26 workshops (75% growth) 12 tutorials (100% growth)

• What’s new?  Paper spotlights every morning (1 min/paper)  All papers are required to have a poster presented.  Networking Session: Building a Career in Data Science

Research Track

Reviewing Process

Submissions per Country

Acceptance by Subject Area

Predicting Paper Acceptance

Predicting Paper Acceptance

Academia VS. Industry

Review Statistics

Review Statistics

Research Topics • Some technical topics that I found especially notable/popular include:  Topic/Graphical modeling (not only for text mining, many tasks are addressed with this method)  Deep Learning (2 tutorials, but no full papers)  Social Networks and graph analytics (popular for the last 10 years, and even more so this year)  Recommendations  Workforce analytics

Best Paper Awards • Best paper: Reducing the Sampling Complexity of Topic Models. Aaron Q Li, Carnegie Mellon University; Amr Ahmed, Sujith Ravi, Alexander J Smola, Google.

• Best student paper: An Efficient Algorithm For Weak Hierarchical Lasso Yashu Liu, Jie Wang, Jieping Ye, Arizona State University,Arizona State University.

Test of Time Award • Integrating Classification and Association Rule Mining [KDD 1998], cited by over 2000 times.

Some interesting papers • Mining Topics in Documents: Standing on the Shoulders of Big Data. Zhiyuan Chen, Bing Liu; University of Illinois at Chicago;

• Matching Users and Items Across Domains to Improve the Recommendation Quality. Chung-Yi Li,Shou-De Lin; National Taiwan University • FoodSIS: A Text Mining System to Improve the State of Food Safety in Singapore Kiran Kate, Sneha Chaudhari, Andy Prapanca, Jayant Kalagnanam; IBM Research;

• Mining Topics in Documents: Standing on the Shoulders of Big Data. Zhiyuan Chen, Bing Liu; University of Illinois at Chicago;

• Proposed a variant of topic model that can generate more accurate and coherent topics via integrating knowledge. • 2 kinds of Knowledge:  Must-links, e.g. ,  Cannot-links, e.g. ,

• Knowledge are mined through frequent itemset mining. • But knowledge can be wrong, authors further propose some rules to clean up the knowledge. • Knowledge can be easily integrated the into the inference algorithm with generalized Polya Urn Model.

Innovation Award Talk • Principles of Very Large Scale Modeling by Pedro Domingos, from University of Washington.

• Three principles:  1. Model the whole, not just parts; People (customers) influence each other - model the whole network, not each person separately.

 2. Tame complexity via hierarchical decomposition; We can make 2 assumptions: 1) Subparts are independent given the part; 2) Probability for class is the avg over subclasses. Using hierarchy and 2 previous assumptions makes our inference tractable. Example: Markov Logic Network + Sum-Product Theorem = Tractable Markov Log

 3. Time and space should not depend on data size.

THANK YOU! Video recordings of KDD: http://videolectures.net/kdd2014_newyork/