Abstract
Search and recommender systems should prioritize support for user tasks over support for individual queries or actions. To that end, the Information Retrieval (IR) community has spent decades trying to understand users' tasks and their contexts, and how to best assist users in making progress toward completing them. The recent advancements in generative artificial intelligence (AI) have drastically shifted the landscape of task-focused IR. Users can now express not only their queries and questions, but actual information needs, tasks, and goals in natural language to an AI system and receive not just results, but also answers in natural language that are generated specifically for them. This new paradigm raises many interesting questions, opportunities, and challenges. We brought together a group of highly motivated students and scholars in a two-day workshop on the Microsoft campus in Redmond to discuss these issues, learn from each other, and envision a new future for task-focused IR and information access more broadly. Date : 28--29 September 2023. Website : https://ir-ai.github.io.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
