Organizers: Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Yang and
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.
Organizers: Hongshen Chen, Zhaochun Ren, Pengjie Ren, Dawei Yin, Xiaodong
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.
Organizers: Changlong Sun, Yating Zhang, Xiaozhong Liu, Fei Wu
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.
Organizers: Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, Chirag Shah
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.
Organizers: Haiming Liu, Ingo Frommholz and Massimo Melucci
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
Organizers: Bo Long, Jieping Ye, Zang Li, Huiji Gao, Sandeep Kumar Jha
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.
Organizers: Dr. Fuli Feng, Dr. Cheng Luo, Prof. Xiangnan He, Prof. Yiqun
Liu, Prof. Tat-Seng Chua
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.
Organizers: Dietmar Jannach, Surya Kallumadi, Tracy Holloway King, Weihua
Luo, Shervin Malmasi,
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.