Workshops at SIGIR 2020, Xi’an, China
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances
Organizers: Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Yang and
Alex Beutel
URL: https://drl4ir.github.io/
A Short Description: Information retrieval (IR) techniques, such as search,
recommendation
and online advertising, satisfying users’ information needs by suggesting users personalized
objects (information or services) at the appropriate time and place, play a crucial role in
mitigating the information overload problem. Since the widely use of mobile applications,
more and more information retrieval services have provided interactive functionality and
products. Thus, learning from interaction becomes a crucial machine learning paradigm for
interactive IR, which is based on reinforcement learning. With recent great advances in deep
reinforcement learning (DRL), there have been increasing interests in developing DRL based
information retrieval techniques, which could continuously update the information retrieval
strategies according to users’ real-time feedback, and optimize the expected cumulative
long-term satisfaction from users. Our workshop aims to provide a venue, which can bring
together academia researchers and industry practitioners (i) to discuss the principles,
limitations and applications of DRL for information retrieval, and (ii) to foster research
on innovative algorithms, novel techniques, and new applications of DRL to information
retrieval.
The SIGIR 2020 Workshop on Applied Interactive Information Systems
Organizers: Hongshen Chen, Zhaochun Ren, Pengjie Ren, Dawei Yin, Xiaodong
He
URL: https://aiis.newidea.fun/
A Short Description: Nowadays, intelligent information systems, especially
the interactive information systems (conversational interaction systems; news feed
recommender systems, and interactive search engines, etc.), are ubiquitous in real-world
applications. These systems either converse with users explicitly through natural languages,
or mine users interests and respond to users requests implicitly. Interactivity has become a
crucial element towards intelligent information systems. Despite the fact that interactive
information systems have gained significant progress, there are still many challenges to be
addressed when applying these models to real-world scenarios. This half day workshop
explores challenges and potential research, development, and application directions in
applied interactive information systems. We aim to discuss the issues of applying
interactive information models to production systems, as well as to shed some light on the
fundamental characteristics, i.e., interactivity and applicability, of different interactive
tasks. We welcome practical, theoretical, experimental, and methodological studies that
advances the interactivity towards intelligent information systems. The workshop aims to
bring together a diverse set of practitioners and researchers interested in investigating
the interaction between human and information systems to develop more intelligent
information systems.
The 1st International Workshop on Legal Intelligence
Organizers: Changlong Sun, Yating Zhang, Xiaozhong Liu, Fei Wu
URL: https://legalai2020.github.io/
A Short Description: In the digital era, information retrieval,
text/knowledge mining, and NLP techniques are playing increasingly vital roles in legal
domain. While the open datasets and innovative deep learning methodologies provide critical
potentials, in the legal-domain, efforts need to be made to transfer the
theoretical/algorithmic models into the real applications to assist users, lawyers, judges
and the legal professions to solve the real problems. The objective of this workshop is to
aggregate studies/applications of text mining/retrieval and NLP automation in the context of
classical/novel legal tasks, which address algorithmic, data and social challenges of legal
intelligence. Keynote and invited presentations from industry and academic will be able to
fill the gap between ambition and execution in the legal domain.
The 3rd International Workshop on ExplainAble Recommendation and Search (EARS 2020)
Organizers: Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, Chirag Shah
URL: https://ears2020.github.io/
A Short Description: The motivation of the workshop is to promote the
research and application of Explainable Recommendation and Search, under the background of
Explainable AI in a more general sense. Explainable recommendation and search attempt to
develop models or methods that not only generate high-quality recommendation or search
results, but also intuitive explanations of the results for users or system designers, which
can help improve the system transparency, persuasiveness, trustworthiness, and
effectiveness, etc.
BIRDS - Bridging the Gap between Information Science, Information Retrieval and Data Science
Organizers: Haiming Liu, Ingo Frommholz and Massimo Melucci
URL: https://birds-ws.github.io/birds2020/
A Short Description: We will be running a workshop called BIRDS - Bridging
the Gap between Information Science, Information Retrieval and Data Science, which aims to
foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and
Data Science (DS). Recognising the commonalities and differences between these communities,
the full-day workshop will bring together experts and researchers in IS, IR and DS to
discuss how they can learn from each other to provide more user-driven data and information
exploration and retrieval solutions. Therefore, we welcome submissions conveying
interdisciplinary ideas on how to utilise, for instance, IS concepts and theories in IR
and/or DS approaches to support users in data and information assess. BIRDS will be
collocated with the 43rd International ACM SIGIR Conference on Research and Development in
Information Retrieval (SIGIR 2020) at Xi’an, China
Deep Natural Language Processing for Search and Recommendation
Organizers: Bo Long, Jieping Ye, Zang Li, Huiji Gao, Sandeep Kumar Jha
URL: https://sites.google.com/view/deepnlp2020
A Short Description: Search and recommender systems process rich natural
language text data such as user queries and documents (e.g., articles, profiles,
transcripts, comments, posts). Achieving high-quality search and recommendation results
requires processing and understanding such information effectively and efficiently, where
natural language processing (NLP) technologies are widely deployed. Natural language data
are represented as a sequence of words. Understanding such sequential information is
generally a nontrivial task in traditional methods, with challenges on both data sparsity
and data generalization. Deep learning models provide an opportunity to effectively extract
the representative relevant information, thus better understanding complicated semantics and
underlying user intention. In recent years, the rapid development of deep learning
technology has been proven successful for improving various NLP tasks, indicating their
great potential of promoting search and recommender systems.
Developing deep learning models for NLP in search and recommender systems involves various
fundamental components including 1) query and document understanding that extracts and
infers relevant information, such as intent prediction, entity tagging and disambiguation,
topic understanding and opinion mining; 2) retrieval and ranking methodologies designed with
strong latency restrictions and various matching strategies; and 3) language generation
techniques designed to proactively guide/interact with users to further resolve ambiguity,
such as query reformulation, (i.e., query suggestion, auto-completion, spell correction) and
conversational recommendation. Furthermore, conversational AI provides intelligent user
experience that is beyond current search and recommendation, which can potentially deliver
more values to members. In this workshop, we propose to discuss deep neural network based
NLP technologies and their applications in search and recommendation, with the goal of
understanding (1) Why deep NLP is helpful; (2) What are the challenges to develop and
productionize it; (3) How to overcome the challenges; (4) Where deep NLP models produce the
largest impact.
FinIR 2020: The First Workshop on Information Retrieval in Finance
Organizers: Dr. Fuli Feng, Dr. Cheng Luo, Prof. Xiangnan He, Prof. Yiqun
Liu, Prof. Tat-Seng Chua
URL: https://finir2020.github.io/
A Short Description: The FinIR 2020 workshop explores challenges and
potential research directions about Information Retrieval (IR) in finance. The focus will be
on stimulating discussions around the accessing, searching, filtering, and analyzing various
financial documents, such as the financial reports, analyst reports, filling forms, and news
reports, in various financial scenarios including banking, insurance, and investment. The
workshop aims to bring together a diverse set of researchers and practitioners interested in
theoretical, experimental, and methodological studies on relevant topics. The workshop also
hosts a grand challenge on quantifying analyst reports and news reports in multiple
languages for commodity price prediction.
The 2020 SIGIR Workshop On eCommerce
Organizers: Dietmar Jannach, Surya Kallumadi, Tracy Holloway King, Weihua
Luo, Shervin Malmasi,
URL: https://sigir-ecom.github.io/
A Short Description: The SIGIR Workshop on eCommerce will serve as a
platform for publication and discussion of Information Retrieval and NLP research & their
applications in the domain of eCommerce. This workshop will bring together practitioners and
researchers from academia and industry to discuss the challenges and approaches to product
search and recommendation in eCommerce.