There has been a significant interest in applications that require algorithms that can keep track of the users’ state in the recent years. Examples of such systems include conversational systems and intelligent assistants, contextual information retrieval and advertising systems, intelligent tutoring systems, expertise finding systems or systems that model player expertise in gaming, among others.
This workshop aims at bringing together researchers from academia and industry that focus on different aspects of state-based user modelling to exchange ideas and build state-aware user-centric systems. We encourage submissions to this workshop in a variety of topics aiming to discuss the challenges associated with capturing and effectively utilising user state in different web search and data mining applications.
State-based User Modelling
Inferring and utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent user-centric systems in the real-world. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/matchmaking systems and advertising systems, which all rely on identifying the user state, in order to provide the most relevant information to the user and assist them in achieving their goals.
For example, due to their interactive nature, search and recommender systems are notoriously hard to develop and evaluate, since they involve a multi-step decision-making process, where a stream of interactions occurs between the user and the system. Traditionally, to make the problem tractable, the interactions are often viewed as independent, but in order to improve such systems further, it becomes important to consider and leverage user states.
In the recent years, various interactive machine learning techniques (including bandit based and reinforcement learning based techniques) have been applied for interaction tasks as these approaches allow to learn and continuously change the strategy based on user feedback and other type of rewards.
Even though many such systems would significantly benefit from having a representation of the user’s state, there has been very limited work towards building intelligent learning mechanisms that can be used to identify, represent and update the state of the user.
Hence, devising information retrieval systems that can keep track of the user’s state and make recommendations based on this has been listed as one of the grand challenges of information retrieval that needs to be tackled during the next few years. In this context,we believe it is timely to organize a workshop that revisits the problem of designing and evaluating state-aware user-centric systems and makes sure the community, spanning academic and industrial backgrounds, is working on the right problem.
Goals and Objectives
This workshop aims to facilitate a forum for leveraging intelligent methods for modelling user’s state and its transition in an effective manner. Modelling user’s state is a challenging task with many currently open questions:
- User representation and recommender systems: How to design accurate state-aware representations of the user and embed that information in predictive models?
- Task understanding and supporting user tasks: How to identify the specific sub-task a user is trying to accomplish and design state-aware intelligent systems that assist users in each step of their goals?
- State-aware evaluation: How to develop metrics and evaluation techniques that model and understand user states, so as to provide a more sensitive and enhanced evaluation of the user-centric systems?
- Human in the loop: How to include the human in the learning "loop" in an interactive fashion, making feedback an integral part of the modelling?
- User-aware systems: How to incorporate user state information in systems to achieve better human-machine intelligence?
- Cognitive/contextual user understanding: How to understand from a cognitive point of view how actions taken by users and their context can influence their state?
- State-aware ML algorithms: How do the different ML paradigms which enable a system to identify and leverage user states compare for different application scenarios?
To progress and answer these questions, it is imperative for research communities from cognitive science, intelligent tutoring systems, information retrieval, human computer interaction, psychology and other diverse fields to bring forward their inputs, which is one of the primary goals of this workshop.
We encourage sharing the ongoing work that is geared towards addressing these challenges, thus aiming to improve the research landscape and understanding of this emerging topic.
We aim to provide a platform for budding ideas from different fields to unite together giving inspiration to more powerful user modelling approaches. WSDM, with its attendance by a broad spectrum of cross-disciplinary researchers offers the ideal venue for this exchange of ideas. Topics of interest include areas concerning the above mentioned highlighted points.
- Emine Yilmaz, University College London and Amazon
- John Shawe-Taylor, University College London, UNESCO Chair in Artificial Intelligence
- Sahan Bulathwela, University College London
- Maria Perez-Ortiz, University College London
- Rishabh Mehrotra, Spotify Research
- Colin de la Higuera, Université de Nantes, UNESCO Chair in teacher training technologies with OER
- Davor Orlic, COO, Knowledge 4 All Foundation
Join us @ WSDM’20 on February 7th!
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 firstname.lastname@example.org
- December 1, 2019: deadline for submission of contributions to workshops
- December 15, 2019: paper acceptance notification
- February 7, 2020: WSDM-2020 workshops
List of speakers and detailed schedule coming soon.