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Invited Talks:

Jie Tang, Tsinghua University

Cross-domain Collaboration Recommendation

Abstract: Interdisciplinary collaborations have generated huge impact to society. In particular, with the rapid development of online open source social platforms, many software product have been developed by people from different domains. However, it is often hard for people to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations?Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this talk, I will analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics.

Bio: Jie Tang is an associate professor with Department of Computer Science and Technology, Tsinghua University. His interests include social network analysis, data mining, and machine learning. He published more than 100 journal/conference papers and holds 10 patents. He served as PC Co-Chair of WSDM'15, ASONAM'15, ADMA'11, SocInfo'12, KDD-CUP Co-Chair of KDD'15, Poster Co-Chair of KDD'14, Workshop Co-Chair of KDD'13, Local Chair of KDD'12, Publication Co-Chair of KDD'11, and as the PC member of more than 50 international conferences. He is the principal investigator of National High-tech R&D Program (863), NSFC project, Chinese Young Faculty Research Funding, National 985 funding, and international collaborative projects with Minnesota University, IBM, Google, Nokia, Sogou, etc. He leads the project Arnetminer.org for academic social network analysis and mining, which has attracted millions of independent IP accesses from 220 countries/regions in the world. He was honored with the CCF Young Scientist Award, NSFC Excellent Young Scholar, and IBM Innovation Faculty Award.

Jian-Guang Lou, Software Analytics Group

Service Analytics: An Experience Report

Abstract: As online services become more and more popular, service quality management has become a critical task that aims to minimize the service downtime and to ensure high quality of the provided services. In practice, service management is conducted through analyzing a huge amount of monitoring data collected at runtime of a service. Such data-driven incident management faces several significant challenges such as the large data scale, complex problem space, and incomplete knowledge. To address these challenges, we carried out two-year software-analytics research where we designed a set of novel data-driven techniques and developed a set of industrial system, e.g., Service Analysis Studio (SAS), targeting real scenarios in large-scale online services of Microsoft. The techniques have been deployed to worldwide product datacenters and widely used by on-call engineers for service management. This talk shares our experience about using software analytics to solve engineers¡¯ pain points in service management, the developed data-analysis techniques, and the lessons learned from the process of research development and technology transfer.

Bio: Jian-Guang Lou is now a researcher in Software Analytics Group, Microsoft Research. Before he joined Microsoft Research in Sep. 2003., he obtained my Ph.D degree in the area of video tracking and pattern recognition from Institute of Automation, Chinese Academic of Sciences in 2003 , and Master degree from Zhejiang University in 2000 respectively. During the past decade, he has worked on a series of cross-discipline projects including multi-view video systems (multi-view video capturing, compressing and streaming over IP network), peer-to-peer streaming systems, and data mining for software systems. Recently, he mainly focused on the research of service analytics to help on-line service engineers to improve their daily productivity. Many of his works have been applied and deployed in Microsoft for online service management.