The SIGIR 2018 workshop program will host 10 compelling workshops that highlight the breadth of interesting problems being explored in the field of information retrieval and that explore novel ideas and emerging areas in the field.
Full day workshops
- CAIR’18: Conversational Approaches to Information Retrieval
- ECOM’18: eCommerce
- ProfS2018: First International Workshop on Professional Search
- KG4IR’18: Knowledge Graphs and Semantics for Text Retrieval, Analysis and Understanding
Half day workshops
- 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
Please note that SIGIR 2018 does not offer a registration option for workshop or tutorial-only participation.
Muthu Kumar Chandrasekaran (NUS), Kokil Jaidka (U Penn), Philipp Mayr (GESIS)
The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP could help in these search and look-up activities, but are not yet widely used. To this purpose, we propose the third iteration of this Joint workshop. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2018 will also feature the fourth edition of the Computational Linguistics (CL) Scientific Summarization Shared Task besides invited talks and paper sessions
Jaime Arguello (University of North Carolina at Chapel Hill), Hideo Joho (University of Tsukuba), Julia Kiseleva (University of Amsterdam), Filip Radlinski (Google), Damiano Spina (RMIT University)
Recent advances in commercial conversational services that allow naturally spoken and typed interaction, particularly for well-formulated questions and commands, have increased the need for more human-centric interactions in information retrieval. The Second International Workshop on Conversational Approaches to Information Retrieval (CAIR’18) will again bring together academic and industrial researchers to create a forum for research on conversational approaches to search. A specific focus will be on techniques that support complex and multi-turn user-machine dialogues for information access and retrieval, and multi-model interfaces for interacting with such systems. We invite submissions addressing all modalities of conversation, including speech-based, text-based, and multimodal interaction. We also welcome studies of human-human interaction (e.g., collaborative search) that can inform the design of conversational search applications, and work on evaluation of conversational approaches.
Xi Niu (UNC Charlotte), Wlodek Zadrozny (UNC Charlotte), Kazjon Grace (Univ. of Sydney), Weimao Ke (Drexel Univ.)
The concept of surprise is central to human learning and development. However, compared to accuracy, surprise has received little attention in the IR community, yet it is an essential component of the information seeking process. This workshop brings together researchers and practitioners of IR to discuss the topic of computational surprise, to set a research agenda, and to examine how tobuild datasets for research into this fascinating topic. The themes in this workshop include discussion of what can be learned from some well-known surprise models in other fields, such as Bayesian surprise; how to evaluate surprise based on user experience; and how computational surpirse is related to the newly emerging areas, such as fake news detection, computational contradiction, clickbait detection, etc.
Paul Groth (Elsevier Labs), Laura Koesten (The Open Data Institute, Univ. of Southampton), Philipp Mayr (GESIS), Maarten de Rijke (Univ. of Amsterdam), Elena Simperl (Univ. of Southampton)
This workshop explores challenges in data search, with a particular focus on data on the web. We want to stimulate an interdisciplinary discussion around how to improve the description, discovery, ranking and presentation of structured and semi-structured data, across data formats and domain applications. We welcome contributions describing algorithms and systems, as well as frameworks and studies exploring human data interaction. We see a large space for discussion and future research in the development of federated data discovery and search technologies, which leverages recent advances in information retrieval, Semantic Web and databases, and is mindful of human factors. The workshop aims to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly.
Yongfeng Zhang (Rutgers), Yi Zhang (UC Santa Cruz), Min Zhang (Tsinghua Univ.)
The purpose of the workshop is to gather researchers and practitioners of recommendation and search systems to communicate the latest ideas and research achievements on explainable recommendation and search, discuss about the advantages and disadvantages of existing approaches, and share the ideas of future directions of recommendation and search in the explanation perspective. Based on this workshop, we would not only like to present the latest research achievements, but also connect researchers in the community that are interested in the explainable recommendation and search topic to promote this direction in the following years.
Jon Degenhardt (eBay), Pino Di Fabbrizio (Rakuten IT), Surya Kallumadi (Kansas State), Mohit Kumar (Flipkart), Yiu-Chang Lin (Rakuten IT), Andrew Trotman (Univ. of Otago), Huasha Zhao (Alibaba)
eCommerce Information Retrieval has received little attention in the academic literature, yet it is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, and others). SIGIR has for several years seen sponsorship from these kinds of organisations, who clearly value the importance of research into Information Retrieval. The purpose of this workshop is to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, and to examine how to build datasets for research into this fascinating topic.
eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities (the kinds of search seen in when buying are different from those of web-page search (i.e. it is not clear how shopping and buying relate to the standard web-search interaction models)) through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings).
This workshop is a follow up to the “SIGIR 2017 workshop on eCommerce (ECOM17)”, which was organized at SIGIR 2017, Tokyo. In the 2018 workshop, in addition to a data challenge, we will be following up on multiple aspects that were discussed in the 2017 workshop.
Suzan Verberne (Leiden University), Jiyin He (CWI), Udo Kruschwitz (University of Essex), Birger Larsen (Aalborg University), Tony Russell-Rose (UXLabs), Arjen P. de Vries (Radboud University)
Professional search in specific domains has been addressed in IR research over the last decades. Although each domain (e.g. legal, medical, academic, governmental) has its own idiosyncrasies, professional search tasks have specific requirements in common that are different from requirements of generic web search engines. These requirements follow directly from the context and needs of professional searchers: Searchers in different domains often exhibit particular search behavior different from general Web search. These unique behavioral patterns can be both a nature of the profession as well as a result of using a particular professional search tool.
This workshop will address the specific requirements for professional search from multiple angles; covering many different facets of professional search in an interactive setting where researchers work with input from information professionals to their mutual benefit. The workshop will deliver a roadmap of research directions for the years to come.
Lingyu Zhang (DiDi Chuxing), Zhenhui Li (Penn State Univ.), Wei Ai (Univ. of Michigan)
This workshop at SIGIR 2018 is for professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation to improve the transportation system. We plan to have both paper presentations and invited talks. This workshop would be sponsored by Didi Chuxing, with other committee members from the Pennsylvania State University and the University of Michigan. Topics include social media mining for intelligent transportation, searching and ranking in transportation contexts, and classification, recommendation and clustering on traffic networks.
Laura Dietz (Univ. of New Hampshire), Chenyan Xiong (Carnegie Mellon Univ.), Jeff Dalton (Univ. of Glasgow), Edgar Meij (Bloomberg)
Semantic technologies such as controlled vocabularies, thesauri, and knowledge graphs have been used throughout the history of information retrieval for a variety of tasks. Recent advances in knowledge acquisition, alignment, and utilization have given rise to a body of new approaches for utilizing knowledge graphs in text retrieval tasks and it is therefore time to consolidate the community efforts and study how such technologies can be employed in information retrieval systems in the most effective way. It is also time to start and deepen the dialogue between researchers and practitioners in order to ensure that breakthroughs, technologies, and algorithms in this space are widely disseminated. The goal of this workshop is to bring together and grow a community of researchers and practitioners who are interested in using, aligning, and constructing knowledge graphs and similar semantic resources for information retrieval applications.
Hamed Zamani (UMass Amherst), Mostafa Dehghani (Univ. of Amsterdam), Fernando Diaz (Spotify), Hang Li (Toutiao AI Lab), Nick Craswell (Microsoft)
In recent years, machine learning approaches, and in particular deep neural networks, have yielded significant improvements on several natural language processing and computer vision tasks; however, such breakthroughs have not yet been observed in the area of information retrieval. Besides the complexity of the IR tasks, such as understanding the user’s information needs, a main reason is the lack of high-quality and/or large-scale training data for many IR tasks. This necessitates studying how to design and train machine learning algorithms where there is no large-scale or high-quality data in hand. Therefore, considering the quick progress in development of machine learning models, this is an ideal time for a workshop that especially focuses on learning in such an important and challenging setting for IR tasks. The goal of this workshop is to bring together researchers from industry—where data is plentiful but noisy—with researchers from academia—where data is sparse but clean to discuss solutions to these related problems.