User-Adaptive Search and Evaluation for Complex Information-Seeking Tasks

Project description

While evaluation of web search engine effectiveness is relatively well-understood, measuring information retrieval
performance in the context of complex tasks with heterogeneous users is a largely neglected problem. This
project will develop a new evaluation framework to understand and characterise users and their situation within
complex, multi-faceted search tasks, exemplified through job-search. User-specific characteristics and situations
will be mined from complex profiles and interaction logs for online information services run by the industry partner,
SEEK. The new techniques will redefine understanding of task-oriented search, and has the potential to reinvent
the user experience for such complex search tasks.

Project team

Leader: Tim Baldwin

Staff: Alistair Moffat, Justin Zobel

Collaborators: Lawrence Cavedon (RMIT), Mark Sanderson (RMIT), Falk Scholer (RMIT), Wilson Wong (SEEK), Evelyn Balfe (SEEK)

Sponsors: SEEK

Other projects

Networks and data in society projects

Disciplines

Computing and Information Systems

Domains

Networks and data in society

Keywords

Information retrieval; machine learning; natural language processing