1. KDD’14 Debrief 24 th April - 27 st, 2014 24 th April - 27 st August, 2014 New York City, US WING Monthly Meeting (Oct 24, 2014) Presented by Xiangnan He

2. Open Ceremony 2

3. 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

4. Overview The largest KDD conference ever. 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? 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

5. Research Track

6. Reviewing Process

7. Submissions per Country

8. Acceptance by Subject Area

9. Predicting Paper Acceptance

11. Academia VS. Industry

12. Review Statistics

14. 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

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

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

17. Some interesting papers Mining Topics in Documents: Standing on the Shoulders of Big Data. 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. 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;

18. Mining Topics in Documents: Standing on the Shoulders of Big Data. 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. Proposed a variant of topic model that can generate more accurate and coherent topics via integrating knowledge. 2 kinds of Knowledge: 2 kinds of Knowledge:  Must-links, e.g.,  Must-links, e.g.,  Cannot-links, e.g.,  Cannot-links, e.g., Knowledge are mined through frequent itemset mining. Knowledge are mined through frequent itemset mining. But knowledge can be wrong, authors further propose some rules to clean up the knowledge. 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. Knowledge can be easily integrated the into the inference algorithm with generalized Polya Urn Model.

19. Innovation Award Talk Principles of Very Large Scale Modeling Principles of Very Large Scale Modeling by by Pedro Domingos, from University of Washington. Three principles: Three principles: 1. Model the whole, not just parts; 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; 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. 3. Time and space should not depend on data size.

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