Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Abstract

It is our great pleasure to welcome you to the 2016 ACM Conference on Knowledge Discovery and Data Mining -- KDD'16. We hope that the content and the professional network at KDD'16 will help you succeed professionally by enabling you to: identify technology trends early; make new/creative contributions; increase your productivity by using newer/better tools, processes or ways of organizing teams; identify new job opportunities; and hire new team members. We are living in an exciting time for our profession. On the one hand, we are witnessing the industrialization of data science, and the emergence of the industrial assembly line processes characterized by the division of labor, integrated processes/pipelines of work, standards, automation, and repeatability. Data science practitioners are organizing themselves in more sophisticated ways, embedding themselves in larger teams in many industry verticals, improving their productivity substantially, and achieving a much larger scale of social impact. On the other hand we are also witnessing astonishing progress from research in algorithms and systems -- for example the field of deep neural networks has revolutionized speech recognition, NLP, computer vision, image recognition, etc. By facilitating interaction between practitioners at large companies & startups on the one hand, and the algorithm development researchers including leading academics on the other, KDD'16 fosters technological and entrepreneurial innovation in the area of data science. This year's conference continues its tradition of being the premier forum for presentation of results in the field of data mining, both in the form of cutting edge research, and in the form of insights from the development and deployment of real world applications. Further, the conference continues with its tradition of a strong tutorial and workshop program on leading edge issues of data mining. The mission of this conference has broadened in recent years even as we placed a significant amount of focus on both the research and applied aspects of data mining. As an example of this broadened focus, this year we have introduced a strong hands-on tutorial program nduring the conference in which participants will learn how to use practical tools for data mining. KDD'16 also gives researchers and practitioners a unique opportunity to form professional networks, and to share their perspectives with others interested in the various aspects of data mining. For example, we have introduced office hours for budding entrepreneurs from our community to meet leading Venture Capitalists investing in this area. We hope that KDD 2016 conference will serve as a meeting ground for researchers, practitioners, funding agencies, and investors to help create new algorithms and commercial products. The call for papers attracted a significant number of submissions from countries all over the world. In particular, the research track attracted 784 submissions and the applied data science track attracted 331 submissions. Papers were accepted either as full papers or as posters. The overall acceptance rate either as full papers or posters was less than 20%. For full papers in the research track, the acceptance rate was lower than 10%. This is consistent with the fact that the KDD Conference is a premier conference in data mining and the acceptance rates historically tend to be low. It is noteworthy that the applied data science track received a larger number of submissions compared to previous years. We view this as an encouraging sign that research in data mining is increasingly becoming relevant to industrial applications. All papers were reviewed by at least three program committee members and then discussed by the PC members in a discussion moderated by a meta-reviewer. Borderline papers were thoroughly reviewed by the program chairs before final decisions were made.