While users interact with online services (e.g. search engines, recommender systems, conversational agents), they leave behind fine grained traces of interaction patterns. The ability to understand user behavior, record and interpret user interaction signals, gauge user satisfaction and incorporate user feedback gives online systems a vast treasure trove of insights for improvement and experimentation. More generally, the ability to learn from user interactions promises pathways for solving a number of problems and improving user engagement and satisfaction.
Understanding and learning from user interactions involves a number of different aspects - from understanding user intent and tasks, to developing user models and personalization services. A user's understanding of their need and the overall task develop as they interact with the system. Supporting the various stages of the task involves many aspects of the system, e.g. interface features, presentation of information, retrieving and ranking. Often, online systems are not specifically designed to support users in successfully accomplishing the tasks which motivated them to interact with the system in the first place. Beyond understanding user needs, learning from user interactions involves developing the right metrics and expiermentation systems, understanding user interaction processes, their usage context and designing interfaces capable of helping users.
Learning from user interactions becomes more important as new and novel ways of user interactions surface. There is a gradual shift towards searching and presenting the information in a conversational form. Chatbots, personal assistants in our phones and eyes-free devices are being used increasingly more for different purposes, including information retrieval and exploration. With improved speech recognition and information retrieval systems, more and more users are increasingly relying on such digital assistants to fulfill their information needs and complete their tasks. Such systems rely heavily on quickly learnig from past interactions and incorporating implicit feedback signals into their models for rapid development.
Learning from User Interactions will be a highly interactive full day workshop that will provide a forum for academic and industrial researchers working at the intersection of user understanding, search tasks, conversational IR and user interactions. The purpose is to provide an opportunity for people to present new work and early results, brainstorm different use cases, share best practices, and discuss the main challenges facing this line of research.
All workshop submissions must be formatted according to ACM SIG Proceedings template. Please feel free to include author names & affiliations in the submissions. We welcome submissions in either long or short format spanning 4-6 pages.
Authors should submit original papers in PDF format through the Easychair system.
This is a workshop where discussion is central, and all attendees are active participants. The workshop will include keynote talks to set the stage and ensure all attendees are on the same page. A small number of contributed papers will be selected for short oral presentation (15-10 minutes), all other papers have a 2 minute boaster, and all papers are presented as poster in an interactive poster session.
The results will be disseminated in various ways:
Rishabh Mehrotra (Spotify Research; University College London)
Emine Yilmaz (University College London; Alan Turing Institute)
Ahmed Hassan Awadallah (Microsoft Research)