Programme

Location: Hyatt Regency Houston/Galleria | Room: Regency C (Level 2)

Proceedings

Call for papers

SUM’20 Workshop is an event co-located within the WSDM conference on February 7th, 2020, in Houston, TX (USA).

A full-day workshop is organized for experts in Artificial Intelligence, Web Search and Data Mining to come together to address the timely topic of state-based user modelling.

The expected outcome of the workshop is to facilitate a forum for leveraging intelligent methods for modelling user's state and its transition in an effective manner. We welcome three categories of papers:

• Research papers present novel methods and use cases of leveraging state-based user modelling. They may present experimental breakthroughs and exemplary application of user state modelling and personalisation. 
• On-going work may cover any aspect of the development of new AI tools and methodologies related to modelling user state. In this case the project or action may not be finished, and, especially in those cases where the expected impact is long-term, the evaluation may be incomplete.
• Position/Vision papers may concern untouched challenges or the use of technologies which up to now have not been tested on the ground.

We welcome technical contributions and position papers from a wide range of topics, including but not limited to:

• User representation and recommender systems
• Task understanding and supporting user tasks
• State-aware evaluation
• Human in the loop
• User-aware systems
• Cognitive/contextual user understanding
• State-aware ML algorithms

Papers will appear in a workshop proceedings, which will be made publicly available online at the workshop website for no charge. Accepted papers will have a presentation (oral and/or poster) at the workshop. Papers must be submitted in PDF according to the new two-column ACM format published in the ACM guidelines, selecting the generic "sigconf" sample. Papers should be no more than six pages in length, including diagrams, appendices and references. SUM’20 submissions should be double-blind. 

All papers should be a maximum 6 pages in length. Position papers can be shorter (2 pages). In all cases, one additional page of cited references is allowed. All submissions and reviews will be handled electronically. Papers must be submitted through Easychair by 23:59, AoE (Anywhere on Earth) on December 1st, 2019.

For inquires about the workshop and submissions, please email to info@k4all.org 

Abstract

User engagement plays a central role in companies and organisations operating online services. A main challenge is to leverage knowledge about the online interaction of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are defining the right metrics and properly optimising for them. A common way that engagement is measured and understood is through the definition and development of metrics of user satisfaction, which can act as proxy of short-term user engagement, mostly at session level. In the context of recommender systems, developing a better understanding of how users interact (implicit signals) with them during their online session is important for developing metrics of user satisfaction. Detecting and understanding implicit signals of user satisfaction are essential for enhancing the quality of the recommendations. This talk will present various works and personal thoughts on how to develop metrics of user engagement, which recommender systems can optimize for. An important message was that, for recommender systems to work both in the short and the long-term, it is important to consider the heterogeneity of both user and content to formalise the notion of engagement, and in turn design the appropriate metrics to capture these and optimize for. One way to achieve this is to follow these four steps: 1) Understanding intents; 2) Optimizing for the right metric; 3) Acting on segmentation; and 4) Thinking about diversity.

Speaker bio

Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in Personalization. Before that, she was a Director of Research at Yahoo, where she led a team of researchers working on advertising quality for Gemini, Yahoo native advertising platform. She also worked with various teams at Yahoo on topics related to user engagement in the context of news, search, and user generated content. Prior to this, she held a Microsoft Research/RAEng Research Chair at the School of Computing Science, University of Glasgow. Before that, she was Professor of Information Retrieval at the Department of Computer Science at Queen Mary, University of London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now audio. She has given numerous talks and tutorials on these and related topics, including recently a WWW 2019 tutorial on "Online User Engagement: Metrics and Optimization". She is regularly a senior programme committee member at conferences such as WSDM, KDD, WWW and SIGIR. She was co-programme chair for SIGIR 2015, WWW 2018 and WSDM 2020.

Abstract

As spoken conversational assistants operate in increasingly complex domains, predicting user satisfaction in conversational systems has become critical. In particular, online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user’s engagement with the agent. Measuring immediate changes in user satisfaction also enables for natural user studies without explicit disruptions to user experience. I will discuss our progress on conversational satisfaction modeling, and applications to automatically evaluating the effects new agent features, e.g., conversation topic suggestion, design choices, e.g., new recommendation phrasing, and subtle VUI features, e.g., prosody modulation for agent responses.

Speaker bio

Dr. Eugene Agichtein is a Winship Associate Professor of Computer Science at Emory University in Atlanta, USA, where he leads the Intelligent Information Access Laboratory (IR Lab). Since January 2019, he has been an "Amazon Scholar” at Amazon Alexa. Eugene's research spans the areas of information retrieval, natural language processing, data mining, and human computer interaction, most recently focusing on conversational search and recommendation. Together with colleagues and students, Eugene published over 100 papers, and has been recognized by multiple awards, including A.P. Sloan Fellow and the 2013 Karen Spark Jones Award from the British Computer Society, and "test of time" and best paper awards from the SIGIR and WSDM conference. He was Program Co-Chair of the WSDM 2012 and WWW 2017 conferences. Website: http://www.cs.emory.edu/~eugene/.

Abstract

Information workers spend significant amounts of their time managing personal information such as communications, documents and tasks. A recent study reported that the average information worker spends an estimated 28 percent of the workweek managing e-mail and nearly 20 percent looking for internal information or tracking down colleagues who can help with specific tasks. This highlights the potential value intelligent technologies could provide by realizing faster and more effective collaboration and information sharing. The use of artificial intelligence in online services has been steadily increasing. Over the past couple of years, we have worked on techniques for understanding user interaction in communication, information sharing and task management to create new intelligence experiences that can help us be more productive. In this talk, I will present an overview of this line of research.

Speaker bio

Ahmed Awadallah is a Principal Research Manager at Microsoft Research AI. He leads the Language & Information Technologies team which focuses on enabling machines to understand and communicate in natural language, answer complex questions, assist with task completion and understand how people interact with information.  Before joining Microsoft Research, Ahmed received his Ph.D. in Computer Science and Engineering from the University of Michigan Ann Arbor and spent some time working at IBM studying a variety of natural language processing problems. Ahmed published 80+ peer-reviewed and is an  inventor on 25 (granted and pending) patents. His past research focused on understanding, measuring and improving  Web search systems. More recently his research focuses on creating scalable machine learning models for NLP and building natural language interfaces to services and data. More details can be found at: https://aka.ms/ahmed

Abstract

Accurate representation of user state plays a critical role in understanding user needs and providing them with accurate information at the correct time. In this talk, I will talk about two applications in which incorporating user state could be highly important: task based information retrieval and recommendation of educational resources. In the first part of the talk, I will describe the work we have done on incorporating representations of user state to task based search engines and show how one can use these representations to predict user needs. I will then move on to recommendation of educational resources, describe a way of representing user state (skills) and describe methods that can be used to match learners with educational resources based on their skill level.

Speaker bio

Emine Yilmaz is a Turing Fellow and Professor at University College London (UCL), Department of Computer Science, as well as an Amazon Scholar at Amazon. Between 2012 and 2019, she also worked as a research consultant for Microsoft Research Cambridge, where she used to work full as a researcher prior to joining UCL. Emine's current research interests include information retrieval, data mining and applications of machine learning. Her research until now has received several awards including a Bloomberg Data Science Research Award in 2018, the Karen Sparck Jones Award in 2015 and the Google Faculty Research Award in 2014. She has published research papers extensively at major venues such as ACM SIGIR, CIKM and WSDM, gave several tutorials as part of top conferences, and organized various workshops. She has served in various roles including co-editor-in-chief for the Information Retrieval Journal, PC Chair for ECIR 2020, ACM SIGIR 2018 and ACM ICTIR 2017 Conferences, Practice and Experience Chair for ACM WSDM 2017, and as the Doctoral Consortium Chair for ECIR 2017. She is also one of the recipients of the prestigious EPSRC Fellowship.

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Workshop motivation

The motivation supporting this workshop is that contributing to the SDGs through AI could represent a win-win approach in which not only the goals themselves receive due attention but moreover this attention will be shared and integrated and allow a fruitful North-South cooperation.

The workshop will provide a forum for the presentation and discussion of analyses of projects, initiatives and existing research networks with emphasis on identifying a portfolio of AI projects addressing SDGs. Particular interest will be placed on presentations that consider alternative ways of using AI for SDGs where it can be argued that they will aim towards creating communities around the SDG themes and thus introduce the sustainability and scalability of AI research networks. The workshop will focus on understanding how and when AI is successful in addressing the different SDG challenges and will aim at providing opportunities to integrate more research expertise –and more diverse research expertise in these tasks.

We also hope to assess and deepen our understanding of AI capacity and use in the North-South relationship with the aim of developing a capacity building agenda. An overall medium term objective is to establish a Global Network of Excellence in AI cross-cutting all regions of the world to address and contribute to sustainable development through the responsible and inclusive design and deployment of AI for SDGs. The goal of such a global network will be to engrain the principles of the SDGs into the awareness of AI researchers.

Building a Global Network of AI researchers

A recent common effort by ICDE, UNESCO and Knowledge 4 All Foundation (K4A) including the newly established UNESCO Chair in AI was a mapping of AI talents, actors, and knowledge hot spots in the Global south, which illustrates the extent to which universities, start-ups, and other sectors already engage with AI in 4 regions: ASIA, LAC, MENA, and SSA. The results show a surprising number of 600 entities from 33 countries.

This resulted in the decision of launching the Network of Research Excellence in Artificial Intelligence in Africa with the purpose of providing a better understanding of the current state of capacities across Sub-Saharan Africa to engage in AI for development. The idea now is to expand, scale and sustain a Global Network of Excellence, and mobilize it towards addressing all SDGs.

To this end, we are investing and design a range of AI for development initiatives, focusing on innovations, foundations and governance. These initiatives will support relevant research, develop AI applications that are inclusive, ethical, and rights-based, and strengthen and create appropriate capacity building programs.

Global sustainability agenda

In 2015 the United Nations General Assembly approved the important document prepared by UNESCO in which it defined 17 goals for the 21st century. These Sustainable Development Goals or SDGs address what should be considered as the most important issues for humanity and society. They are used today as a roadmap by many institutions and, in particular, by the UNESCO Chairs over the world who have the task of promoting these questions and contributing from the perspective of education and research.

AI and Sustainabiltiy

Recent developments in AI have managed to attract the attention of stakeholders beyond the research communities and industries. Policymakers in international IGOs, NGOs such as UNESCO, OECD, European Commission and a number of UN Member State governments have embraced the idea of AI with a great deal of furore as well as skepticism. All these players are setting up working groups in order to understand better the implications of AI on a larger scale and its positive and negative implications to their own nations and global citizens.

It is therefore understandable that SDGs and AI should meet: as data is being produced and solutions are being shared, quite unsurprisingly academia and industry have been taking up the challenge of meeting the SDGs with artificial intelligence driven solutions.

Inequalities and AI divide

Yet a great key challenge that could deepen inequalities across the globe is the so-called AI divide. The clear majority of AI experts are in North America, Europe and Asia. Other regions and sub-regions are much less represented among the expertise pool, and though there are many new AI-related initiatives emerging around continents, this expertise remains essentially unseen by Northern technology hubs.

Such lack of diversity can entrench unintended algorithmic biases and build discrimination into AI products and result in a deficit of educated practitioners. And these are not the only gaps: for example in the case of Africa, fewer African AI researchers and engineers means fewer opportunities to use AI to improve the lives of Africans.

Knowledge 4 All Foundation and UNESCO Chairs are organizing a 1-day workshop of experts in Artificial Intelligence addressing scalable solutions or exemplars for SDGs with an emphasis on creating potential sub-networks of researchers around each of the 17 SDGs.

The result of the workshop will be to build the global ML/AI landscape for SDGs, understand the scope of a potential Network of Excellence in AI across all regions and provide inputs for an AI roadmap for SDGs, including designing a viable plan for cross-continent cooperation.

We solicit three categories of papers: research papers, on-going work papers, and position/vision papers.

• Research papers present use cases of AI for the SDGs. They may present platforms or systems or in-the-field projects; they may require the development of novel AI tools.
• On-going work may cover any aspect of the development of new AI tools and methodologies to address SDGs. In this case the project or action may not be terminated, and, especially in those cases where the expected impact is long-term, the evaluation may be incomplete.
• Position/Vision papers may concern untouched challenges or the use of technologies which up to now have not been tested on the ground.

Topics should address (at least some of) the UN Sustainable Development Goals:

• Goal 1: End poverty in all its forms everywhere
• Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture
• Goal 3: Ensure healthy lives and promote well-being for all at all ages
• Goal 4: Ensure inclusive and quality education for all and promote lifelong learning
• Goal 5: Achieve gender equality and empower all women and girls
• Goal 6: Ensure access to water and sanitation for all
• Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all
• Goal 8: Promote inclusive and sustainable economic growth, employment and decent work for all
• Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation
• Goal 10: Reduce inequality within and among countries
• Goal 11: Make cities inclusive, safe, resilient and sustainable
• Goal 12: Ensure sustainable consumption and production patterns
• Goal 13: Take urgent action to combat climate change and its impacts
• Goal 14: Conserve and sustainably use the oceans, seas and marine resources
• Goal 15: Sustainably manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss
• Goal 16: Promote just, peaceful and inclusive societies
• Goal 17: Revitalize the global partnership for sustainable development

All papers should be a maximum 6 pages in length. Position papers can be shorter (2 pages). One additional page of cited references is allowed. You may also attach full papers of any format or length that supplement your submission, but we do not guarantee to take these into account in the reviewing process. You may submit work that has previously been published — but please give details of how it has previously been published. Our review process will not be blind: please submit your contributions as PDFs containing the authors’ names.

In order to encourage submissions from as many regions as possible conditional submissions are possible: the authors may send in their contribution even if the financial backing necessary to attend the conference is not yet secured. The organizers will attempt to help find sponsors.

Submission is via Easychair here.

We intend to make accepted contributions available online, linked to the workshop page. There will be no formal published proceedings.

Important dates

Apr 26, 2019: deadline for submission of contributions to workshops
May 10, 2019: paper acceptance notification
Aug 10-12, 2019: IJCAI-2019 Workshops

Programme Committee

• Colin de la Higuera, Université de Nantes, UNESCO Chair in teacher training technologies with OER
• Davor Orlic, COO, Knowledge 4 All Foundation
• Vukosi Marivate, University of Pretoria, ABSA UP Chair of Data Science, Deep Learning Indaba community
• Kathleen Siminyu, Africa's Talking, Deep Learning Indaba community
• Mitja Jermol, Jozef Stefan Institute, UNESCO Chair on Open Technologies for OER and Open Learning
• Maria Fasli, University of Essex, UNESCO Chair in Analytics and Data Science
• John Shawe-Taylor, University College London, UNESCO Chair in Artificial Intelligence
• Marko Grobelnik, Jozef Stefan Institute, Digital Champion Republic of Slovenia
• James Hodson, CEO, AI for Good Foundation, Senior Researcher, Jozef Stefan Institute Artificial Intelligence Lab
• Emine Yilmaz, University College London
• Marcelino Caberera Giraldez, European Commission, Joint Research Centre (JRC-Seville)

Abstracts due October 23rd;  acceptances sent November 7th

Within the past few years, social media have become dominant aggregators and distributors of news. Much public discussion has moved online to social media such as Facebook, Twitter, Reddit, and comment boards. Traditional newspapers and news channels have lost influence to new online forums with weaker editorial controls.

Perhaps as a result, fake news and lies spread fast and widely. Online political discussion is polarised and tribal. Echo-chambers reinforce one-sided views without presenting any balancing alternatives. False rumours persist even when disproved.

One original promise of the Internet was that it would empower better democratic discussion, with wider participation, and better universal access to true news and argument.  If this has not happened, what technologies can we build to achieve it?

Best Paper Award prize is $1,000 USD

We invite contributions on any of these or related topics:

Fake news and fact checking:

• Tracing widely distributed content back to its origins
• Enhancing content in real time with fact checking
• Proactive identification of news trends
• Monitoring, detection, and moderation of polarizing topics

Presenting and organising content:

• Defining ‘fair discussion’ and algorithmic fairness
• Voting and reputation systems for collective evaluation and prioritisation
• Identifying and mitigating tribalism and echo chambers
• Algorithmic fairness in news retrieval and presentation

Abuse, hate speech, and illegal content:

• Identifying abuse and breaches of rules for civil discussion
• Detecting and mitigating tribalism
• New tools for moderators

Collective intelligence:

• How can the quality of on-line discussion be evaluated?
• Improved technologies for online discussion that enable better collective intelligence
• Models of online discussion as large-scale message passing analogous to graphical models

Please send an abstract of up to two pages summarising your contribution; one additional page of cited references is allowed. You may also attach full papers of any format or length that supplement your submission, but we do not guarantee to take these into account in the reviewing process. You may submit work that has previously been published — but please give details of how it has previously been published. Our review process will not be blind: please submit your contributions as PDFs containing the authors’ names.

We intend to make accepted contributions available online, linked to the workshop page. There will be no formal published proceedings.

Important Information

• Submission deadline: October 23^th
• Submissions are closed
• Acceptance decisions sent: November 7^th
• Date of Workshop: December 9^th 2017

Any enquiries should be sent to info@k4all.org email address

Contributions

Either theoretical or practical - are welcomed in relevant areas including but not limited to the following directions:

Analysis of media:

• Detection and prediction of emerging trends;
• Detection of tribalism among online personas;
• Detection of trolling, abuse, and fake news;
• Intelligent tools for discussion moderators

Enhancement of content:

• Automatic fact-checking;
• Annotation according to viewpoint;
• Visualisation and navigation of large on-line discussions

Enhancement of discussion:

• Adapting improved message-passing algorithms for social media;
• Gamification architectures and their effects on discussion;
• Mitigation of tribalism

Algorithmic fairness:

• What does it mean for an algorithm to be ‘fair’ in content prioritization?

• Nicolo Cesa-Bianchi, Università degli Studi di Milano
• Massimiliano Pontil, Istituto Italiano di Tecnologia and University College, London
• John Shawe-Taylor, University College, London
• Chris Watkins, Royal Holloway, London
• Emine Yilmaz, University College, London

• Chris Watkins, Royal Holloway, London
• Emine Yilmaz, University College, London
• Sebastian Riedel, University College, London
• Andreas Vlachos, University of Sheffield
• Marko Grobelnik, Jozef Stefan Institute

Philippe Beaune, ENS Mines Saint-Etienne, France
Leonor Becerra-Bonache, University of Saint-Etienne, France
Olivier Boissier, ENS Mines Saint-Etienne, France
Alexander Clark, Royal Holloway University of London, UK
Colin de la Higuera, Nantes University, France
Ricard Gavaldà, Technical University of Catalonia, Spain
Amaury Habrard, University of Saint-Etienne, France
Jeffrey Heinz, University of Delaware, USA
Adrian Horia Dediu, “Politehnica” University of Bucharest, Romania
Jean Christophe Janodet, University of Evry, France
María Dolores Jiménez-López, Rovira i Virgili University, Spain
José Oncina, University of Alicante, Spain
Alfonso Ortega, Autonomous University of Madrid, Spain
Marc Sebban, University of Saint-Etienne, France
Menno van Zaanen, Tilburg University, Netherlands
György Vaszil,University of Debrecen, Hungary