Leading Information Retrieval Conference Features Keynotes, Presentations from Top Researchers and Technology Companies
ACM SIGIR 2018 Conference Showcases Research, Advanced Theories and Applications in One of Computing’s Fastest-Growing Areas
NEW YORK June 28, 2018 – The 41st International ACM Conference on Research and Development in Information Retrieval (SIGIR 2018), presented by the Association for Computing Machinery’s Special Interest Group on Information Retrieval (SIGIR), will be held from July 8-12 at the University of Michigan in Ann Arbor. The most significant conference in the field, SIGIR 2018 will bring together more than 700 information retrieval (IR) professionals to explore new research, systems and techniques that are changing how organizations and individuals access information.
Workshops and tutorial tracks that reflect the breadth of compelling problems being explored in IR, conducted alongside the presentation of academic research and papers, will include sector-specific applications for industries ranging from healthcare and legal services to retail and transportation. Top technical leaders of companies that include Airbnb, Alibaba, Amazon, Baidu, Bloomberg, Criteo, DiDi, eBay, IBM, Microsoft, Naver, Spotify, and Twitter will present their latest innovations.
The field of IR is expansive, touching all industries and almost every aspect of our daily lives and society. From Web search and e-commerce recommender systems to the development of customer service analytics, transportation informatics for autonomous vehicles, and smart assistants like Siri, Google Home and Alexa, IR systems are increasingly prevalent and constantly evolving to better serve societal needs.
“Rooted in the simple pursuit of connecting people with information, IR lies at the intersection of the most traditional and fundamental asset in the history of humanity—knowledge—and some of the most quickly evolving technologies ever seen,” said Kevyn Collins-Thompson, SIGIR 2018 General Co-Chair. “Modern technology presents unique opportunities to lay a foundation for the ways in which information is accessed, interpreted, and utilized in the future.”
“Global society has reached an unprecedented point in the sheer volume of data generated on a daily basis, as well as in our ability to collect, organize, and interpret that information,” added Brian Davison, SIGIR 2018 Program Co-Chair. “The ability to connect people with information with increased efficiency, rapidity, and accuracy will in turn help produce better systems throughout industries and society at large—from our homes to our schools to our offices to our buses, and everywhere in between—and the innovations put forth at SIGIR will no doubt be integral to those efforts going forward.”
The winner of the Gerard Salton Award, which is presented every three years to an individual who has made significant, sustained, and continuing contributions to research in information retrieval, will be announced at the conference.
2018 ACM SIGIR HIGHLIGHTS
Keynote Addresses (partial list)
Data Science for Social Good and Public Policy: Examples, Opportunities, and Challenges
Rayid Ghani (Director of the Center for Data Science & Public Policy and a Senior Fellow at the Harris School of Public Policy and the Computation Institute at the University of Chicago)
Ghani will discuss lessons learned from data science projects with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. He will highlight information retrieval challenges that need to be solved to increase the effectiveness of today’s machine learning and data science algorithms, which in turn will generate social and policy impact in a fair and equitable manner.
Machine Learning @ Amazon
Rajeev Rastogi (Director of Machine Learning, Amazon)
In this talk, Rastogi will provide an overview of areas where Amazon is applying machine learning techniques internally to address product demand forecasting, product search, and information extraction from reviews and associated technical challenges. He will also discuss three applications where Amazon uses a variety of methods to learn semantically rich representations of data, including the use of probabilistic models for product size recommendations and the use of tensor factorization algorithms to detect fake reviews.
Big Data at Didi Chuxing
Jieping Ye (Vice President and Head of AI Labs at Didi Chuxing; Didi Fellow)
In this keynote, Ye will explain how China’s largest ride-sharing platform applies big data and AI technologies to analyze big transportation data and improve the travel experience for millions of users. Didi Chuxing provides transportation services for over 400 million users, and every day their platform generates over 70 TB worth of data, processes more than 40 billion routing requests, and produces over 15 billion location points.
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
Probabilistic Topic Models for Text Data Retrieval and Analysis
SIGIR 2018 Tutorial on Health Search (HS2018) — A Full-day from Consumers to Clinicians
Conducting Laboratory Experiments Properly with Statistical Tools: An Easy Hands-on Tutorial
Information Discovery in E-commerce
Deep Learning for Matching in Search and Recommendation
Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
Utilizing Knowledge Graphs for Text-centric Information Retrieval
Neural Approaches to Conversational AI
Efficient Query Processing Infrastructures
Fusion in Information Retrieval
CAIR’18: Conversational Approaches to Information Retrieval
ProfS2018: First International Workshop on Professional Search
KG4IR’18: Knowledge Graphs and Semantics for Text Retrieval, Analysis and Understanding
BIRNDL’18: Bibliometric-enhanced IR and NLP for Digital Libraries
CompS’18: Computational Surprise in Information Retrieval
DATA:SEARCH’18: Searching Data on the Web
EARS’18: International Workshop on ExplainAble Recommendation and Search
Intelligent Transportation Informatics
Learning from Limited/Noisy data for IR
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