Workshop title: FL4P-WSDM 2022 : The First Workshop on Federated Learning for Private Web Search and Data Mining
Summary of the workshop: Many popular web-based services and data mining applications nowadays leverage the power of machine learning (ML) and artificial intelligence (AI) to ensure effective performance. All of these are made possible because of the huge volume of data constantly generated on various devices, such as PCs/laptops and mobile smartphones.
Centralized ML and AI pose significant challenges due to regulatory and privacy concerns in real-world use cases. Privacy has been traditionally viewed as an essential human right. There have been increasing legislation endeavors on data privacy protection, e.g. European Union General Data Protection Regulation and California Consumer Privacy Act.
Federated learning (FL) is a new paradigm in machine learning that was first introduced by Google in 2017. It aims to address the challenges above by training a global model using distributed data, without the need for the data to be shared nor transferred to any central facility. Despite the clear advantages, there are still many technical challenges waiting to be solved, such as fairness issues, data statistical heterogeneity, communication efficiency and network robustness.
The workshop is targeted on the above and other relevant issues, aiming to create a platform for people from academia and industry to communicate their insights and recent results.
Topics of interest include, but are not limited to, the following: FL algorithm related issues, e.g. adversarial attack, communication compression, algorithm explainability/interpretability, data/device heterogeneity, optimization algorithm advances, personalization, fairness, resource efficiency, and so on; FL and collaborative ML applications, like advertising, query analysis and processing, web healthcare, search engine, log mining, recommender system, blockchain，social network, and others; Other data privacy preservation techniques, such as differential privacy, secure multi-party computing, data/model distillation, data anonymization, etc; Social, operational challenges and legislation issues about privacy in web search and data mining; Datasets and open-source tools for federated and privacy-preserving web search and data mining.
Organizers: Bo Liu (JD Finance America Corporation), Dongjin Song (University of Connecticut), Fabio Pinelli (IMT Lucca), Fabrizio Silvestri (Sapienza University of Rome), Fenglong Ma (Pennsylvania State University), Gabriele Tolomei (Sapienza University of Rome), Heng Huang (University of Pittsburgh)
Workshop webpage: https://fl4p-wsdm.github.io
Workshop title: Personalization and Recommendations in Search (PaRiS)
Summary of the workshop:
In recent years, search experiences have evolved where personalization plays a crucial role in relevance quality and user satisfaction. Though search context plays a big role in determining the relevance of a given result, the utility of a search system for its users can be further enhanced by providing search results as well as recommendations within the search context. A variety of solutions have been developed for search engines in e-commerce systems, streaming/media content providers, social network systems, and even web search systems for such tasks. However, the research discussions around personalization and recommendation for search remain fragmented across different conferences and workshops. We feel that there is a strong need for bringing together researchers and practitioners working on these problems for a robust discussion and sharing of ideas.
This workshop aims for researchers and practitioners from both academia and industry to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending in a search context. It will include but is not limited to the topics such as evaluation, query assistance, retrieval, ranking, context modeling, benchmark data, and system efficiency for search personalization and recommendations within search contexts, for which more effective and efficient solutions can be shared and discussed. We encourage any recent work relevant to the topics of the workshop to be submitted. We expect the workshop to be of interest to large audiences in the research community of information retrieval and machine learning.
Organizers: Sudarshan Lamkhede (Netflix Research), Anlei Dong (Microsoft), Moumita Bhattacharya (Netflix Research), Hongning Wang (Dept. of Computer Science, Virginia University)
Workshop webpage: https://paris2022.github.io/www/index.html
Workshop title: Integrity 2022: 3rd Workshop in Integrity in Social Networks and Media
Summary of the workshop: In this workshop, top researchers and practitioners from academia and industry will engage in a discussion about algorithmic and system aspects of integrity challenges: cases where the content produced and exchanged compromises the quality, operation, and eventually the integrity of the platform. Examples include misinformation, low quality and abusive content and behaviors, and polarization and opinion extremism.
Lluis Garcia-Pueyo (Facebook), Panayiotis Tsaparas (University of Ioannina),
Anand Bhaskar (Facebook), Prathyusha Senthil Kumar (Facebook), Roelof van Zwol (Pinterest),
Timos Sellis (Facebook), Anthony McCosker (Swinburne University), Paolo Papotti, (University of Roma Tre, EURECOM)
Workshop webpage: http://integrity-workshop.org/
Workshop title: PLM4IR: The WSDM 2022 Workshop on Pre-trained Language Model for Information Retrieval
Summary of the workshop: The PLM4IR Workshop will serve as a platform for discussing the challenges in applying pre-trained language (PLM) models for the information retrieval (IR) field as well as the theory behind the models and applications. The aim of this workshop can be multi-fold: 1) To establish a bridge for communications between academic researchers and industrial researchers, 2) To provide an opportunity for researchers to present new works and early results, and 3) To foster research on innovative algorithms, novel techniques, and new applications of PTMs to information retrieval. The WSDM PLM4IR is a half-day workshop taking place on Friday, Feb 25, 2022, in conjunction with WSDM 2022. WSDM PLM4IR’22 will be a virtual workshop.
Organizers: Yixing Fan (ICT, Chinese Academy of Sciences), Xiaohui Xie (Tsinghua University), Dehong Ma (Baidu Inc. ), Shuaiqiang Wang (Baidu Inc.), Jiafeng Guo (ICT, Chinese Academy of Sciences ), Yiqun Liu (Tsinghua University )
Workshop webpage: https://plm4ir.github.io/
Workshop title: The First International Workshop on Computational Jobs Marketplace
Summary of the workshop: Online job marketplaces such as Indeed.com, CareerBuilder, and LinkedIn Inc. have been helping millions of job seekers find their next jobs and thousands of corporations, as well as institutions, fill their opening positions. Along with the increase of market size, there has been a lot of interesting challenges in this domain, such as the drastic increase of work from home or remote work, the imbalance between the demand and supply of the job market, the popularity of independent workers, the capability of helping job seekers on their whole job-seeking journey and career development, the different objectives and behaviors of all major stakeholders in the ecosystem, e.g. job seekers, employers, recruiters, and job agents, to name just a few. This workshop brings academic researchers and industry practitioners together to share early research results. We focus on the state-of-the-art advances in the computational jobs marketplace.
Organizers: Liangjie Hong (LinkedIn Inc.), Mohammed Korayem (CareerBuilder), Haiyan Luo (Indeed)
Workshop webpage: https://compjobs.github.io/
Workshop title: Workshop on Decision Making for Modern Information Retrieval System
Summary of the workshop: Most IR systems consist of pattern recognition and decision-making. In the past decade, the flourishing of deep learning has provided IR with unprecedented opportunities for mining complex signals from collected data, shifting the public focus vastly toward the pattern recognition end. However, making predictions based on the discovered patterns is merely the first step toward information retrieval: the agent must strategically decide how to leverage the knowledge to maximize the various utilities of different parties. This workshop aims to identify and solve the critical challenges of transitioning pattern recognition to decision making in IR, where existing attempts focus primarily on A/B testing and sequential decision-making with reinforcement learning (RL) and multi-armed bandits (MAB).
We wish to unite researchers and practitioners from various backgrounds to identify the emerging challenges, discover the connections from various domains, and study promising solutions of decision-making for modern information retrieval systems. We note that the decision-making strategy may impact either the immediate performance and long-term development of real-world productions. We also pay special attention to the role of human involvement in practical decision making, and how novel applications and business incentives can be motivated by emerging technologies in such as A/B testing, RL, and MAB.
Organizers: Da Xu (Machine Learning Manager at Walmart Global Tech), Jianpeng Xu (Staff Data Scientist at Walmart Global Tech), Jiliang Tang (Associate Professor at Michigan State University), Min Liu (Engineering Manager at LinkedIn), Tobias Schnabel (Senior Researcher at Microsoft Research)
Workshop title: Interactive and scalable information retrieval methods for e-commerce
Summary of the workshop:
ABOUT THE WORKSHOP
Over the past few years, consumer behavior has shifted from traditional in-store shopping to online shopping. For example, eCommerce sales have grown from around 5% of total US sales in 2012 to around 14% in the year 2021 and more than 25% growth in sales globally. This rapid growth of eCommerce has created new challenges and vital new requirements for intelligent information retrieval systems.
- Scalable systems
Since the pandemic hit, eCommerce became an important part of people’s routine and they started using online shopping for the smallest grocery items to big electronics as well as cars. With such a large assortment of products and millions of users, achieving higher scalability without losing accuracy is a leading concern for information retrieval systems for eCommerce.
- Interactive Systems
The diverse buyers make the relevance of the results highly subjective because relevance varies for different buyers. The most suitable and intuitive solution to this problem is to make the system interactive and provide correct relevance for different users. Hence, interactive information retrieval systems are becoming a necessity in eCommerce.
- System improvement
To handle sudden changes in buyers’ behavior, industries adopted existing sub-optimal in-formation retrieval techniques for various eCommerce tasks. Parallelly, they also started exploring/researching for better solutions and in dire need of help from the research community
The objective of this workshop is to bring a diverse set of practitioners and researchers together and encourage them to share their ideas, challenges & solutions, and research. This workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce.
Organizers: Vachik Dave (Walmart Global Tech), Linsey Pang (Walmart Global Tech), Xiquan Cui (The Home Depot), Lingfei Wu (JD.COM Silicon Valley Research Center), Hamed Zamani (University of Massachusetts Amherst), and George Karypis (University of Minnesota Twin-cities)
Workshop webpage: https://isir-ecom.github.io/
Workshop title: MLoG: Machine Learning on Graphs
Summary of the workshop: Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world, we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Therefore, we propose the MLoG at WSDM’22, which provides a venue to gather academic researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.
Organizers: Tyler Derr (Vanderbilt University),Yao Ma (New Jersey Institute of Technology ), Lingfei Wu (JD.COM Silicon Valley), Feng Shi (TigerGraph), Victor Lee (TigerGraph)
Workshop Webpage: https://mlog-workshop.github.io/