Industrial Track

The WWW 2015 Industry Track highlights web research across the spectrum that is happening in industrial research settings. The track features paper-style presentations of research results and ideas from many major companies. The web has proven to be a fertile ground for industrial research, and companies from small to large, from brand new to deeply entrenched, are making significant leaps forward in science and engineering. The Industry Track will close with an exciting pechakucha session (20 slides x 20 secs. per slide) and panel on knowledge graphs, one of the most pervasive directions in web technology across the industry.

Accepted papers

Title Authors and Affiliations
Synonym Discovery for Structured Entities on Heterogeneous Graphs Xiang Ren (University of Illinois at Urbana-Champaign);
Tao Cheng (Microsoft Research)
The World Conversation: Web Page Metadata Generation From Social Sources Omar Alonso (Microsoft);
Sushma Bannur (Facebook);
Kartikay Khandelwal (Microsoft);
Shankar Kalyanaraman (Facebook)
Constrained Optimization for Homepage Relevance Deepak Agarwal (Linkedin);
Shaunak Chatterjee (Linkedin);
Yang Yang (Linkedin);
Liang Zhang (Linkedin)
Temporal Multi-View Inconsistency Detection for Network Traffic Analysis Houping Xiao (SUNY Buffalo);
Jing Gao (SUNY Buffalo);
Deepak S. Turaga (IBM);
Long Hai Vu (IBM); Alain Biem (IBM)
Leveraging Careful Microblog Users for Spammer Detection Spammer Detection Hao Fu (University of Science and Technology of China);
Xing Xie (Microsoft Research);
Yong Rui (Microsoft Research)
Got Many Labels? Deriving Topic Labels from Multiple Sources for Social Media Posts using Crowdsourcing and Ensemble Learning Shuo Chang (University of Minnesota);
Peng Dai (Google Research);
Jilin Chen (Google Research);
Ed Chi (Google Research)
Topological Properties and Temporal Dynamics of Place Networks in Urban Environments Anastasios Noulas (University of Cambridge);
Blake Shaw (Foursquare);
Renaud Lambiotte (University of Namur);
Cecilia Mascolo (University of Cambridge)
Question Classification by Approximating Semantics Guangyu Feng (University of Waterloo);
Kun Xiong (University of Waterloo);
Yang Tang (University of Waterloo);
Anqi Cui (University of Waterloo);
Jing Bai (Microsoft);
Hang Li (Noah’s Ark Lab, Huawei Technologies);
Qiang Yang (Hong Kong University of Science and Technology);
Ming Li (University of Waterloo)