The workshop program of the SIGIR 2021 will host six attractive workshops covering novel ideas and emerging areas in IR and beyond:


  • IR for Children 2000-2020: Where Are We Now?


  • SIGIR 2021 Workshop on eCommerce (ECOM21)
  • 2nd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2021)
  • Sim4IR: Workshop on Simulation for Information Retrieval Evaluation
  • CSR 2021: The 1st International Workshop on Causality in Search and Recommendation
  • DRL4IR: 2nd Workshop on Deep Reinforcement Learning for Information Retrieval

IR for Children 2000-2020: Where Are We Now?

Organizers: Theo Huibers, Monica Landoni, Emiliana Murgia and Maria Soledad Pera

Abstract: Over 20 years ago, Information Retrieval (IR) researchers began their quest for sound IR systems for children. The path was not straightforward. Challenges posed by interface design, relevance determination, diverse contexts, ethics, and many more, were taken up and explored from different perspectives. Large projects such as Puppy-IR gave this field a certain boost; still, there is neither a sound solution for children in the search area in 2021, nor a roadmap to get there. What is the reason for this? Does the field cry out for specific IR solutions developed on a small scale to serve very small sub-fields and specific target groups? Are there some significant unforeseen barriers that hinder researchers? What about obstacles natural to areas of study such as this one that require a multidisciplinary approach or involve protected populations?  With this workshop, we aim to bring together as many key experts as possible from research and industry who focus on IR for children to understand why, unlike other IR areas, this one has not flourished and look for the biggest challenges for the next 10 years. We are not only thinking of traditional researchers and designers, but also of those who develop and use IR systems for fields such as in music, film, and education, as a way to push past this immobility and look at the problem from new, and perhaps more stimulating, perspectives.


SIGIR 2021 Workshop on eCommerce (ECOM21)

Organizers: Surya Kallumadi, Tracy Holloway King, Shervin Malmasi and Maarten de Rijke

Abstract: 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. The theme of this year’s workshop is fairness in search and recommendation for eCommerce. SIGIR eCom is a full day workshop taking place on Thursday, July 15, 2021 in conjunction with SIGIR 2021. SIGIR eCom’21 will be a virtual workshop.


2nd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2021)

Organizers: Ralf Krestel, Hidir Aras, Linda Andersson, Florina Piroi, Allan Hanbury and Dean Alderucci

With the PatentSemTech2021 workshop we continue our series of workshops launched in 2019, aiming to establish a long-term collaboration and a two-way communication channel between the IP industry and academia from relevant fields such as natural-language processing (NLP), text and data mining (TDM) and semantic technologies (ST). Hereby, we hope to enable the exploration and transfer of new knowledge, methods and technologies for the benefit of industrial applications as well as support research in applied sciences for the IP and neighboring domains. Besides encouraging high quality submissions of research papers related to the IP domain, we plan to create a rich and an informative event including invited speakers, expert panel discussions and demos.


Sim4IR: Workshop on Simulation for Information Retrieval Evaluation

Organizers: Krisztian Balog, David Maxwell, Paul Thomas and Shuo Zhang

Abstract: The use of simulation techniques is not foreign to information retrieval. In the past, simulation has been employed, for example, for constructing test collections and for model performance prediction and analysis in a broad array of information access scenarios. Nevertheless, a standardized methodology for performance evaluation via simulation has not yet been developed. The goal of the proposed workshop is to create a forum for researchers and practitioners to promote methodology development and more widespread use of simulation for evaluation by (1) identifying problem settings and application scenarios, (2) sharing tools, techniques, and experiences, (3) characterizing potentials and limitations, and (4) developing a research agenda.


CSR 2021: The 1st International Workshop on Causality in Search and Recommendation

Organizers: Yongfeng Zhang, Xu Chen, Yi Zhang and Xianjie Chen

Abstract: The motivation of the workshop is to promote the research and application of Causal Analysis and Causal Modeling in Information Retrieval tasks, including but not limited to Search, Recommendation, QA, and Dialog. Causality in IR attempts to develop causal models not only to improve the ranking performance but also benefit IR systems in a broader scope of perspectives such as explainability, fairness, robustness, trustworthiness, etc.

In a broader sense, researchers in the broader AI community have also realized the importance of advancing from correlative learning to causal learning, which aims to address a wide range of AI problems in machine learning, machine reasoning, computer vision, autonomous systems, and natural language processing tasks. As an important branch of AI research, it highlights the importance our IR/RecSys communities to advance from correlative modeling to causal modeling in various search, recommendation, QA and dialog systems.


DRL4IR: 2nd Workshop on Deep Reinforcement Learning for Information Retrieval

Organizers: Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin and Grace Hui Yang

Abstract: 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. The DRL4IR 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.


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