Publication
Towards Context-aware Recommender Systems for Supporting Knowledge Workers in Personal and Corporate Information Space
Mahta Bakhshizadeh; Christian Jilek; Heiko Maus; Andreas Dengel
In: Workshop Proceedings of INFORMATIK 2024. AI@WORK Workshop (AI@WORK-2024), located at INFORMATIK Festival 2024, September 24, Wiesbaden, Germany, Gesellschaft für Informatik (GI), 9/2024.
Abstract
Although recommender systems have been impressively progressing in many domains, their usage in supporting knowledge workers has not been explored as much as in other applications. Having the existing challenges and the recent studies addressing this novel application introduced, this paper provides a framework for integrating such systems into existing concepts and technologies for knowledge assistance. As a case study, a sample recommendation scenario according to the proposed framework is simulated on the historical data of a small group of knowledge workers. The collected explicit feedback of participants on the made recommendations from both their personal and corporate information space indicate that while the approach is promising (with 54\% accuracy in recommending relevant information items), there is still considerable potential for improvement in filtering out noise and better modeling user contexts and information needs.
Projects
- CoMem - Corporate Memory
- Managed_Forgetting_Phase_2 - Sustaining Grass-roots Organizational Memories: Methods and Effects of Applying Managed Forgetting in Administrative Corporate Scenarios
- SensAI - Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories