Publication
Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data
Mahta Bakhshizadeh; Heiko Maus; Andreas Dengel
In: Proceedings of the 18th ACM Conference on Recommender Systems. ACM Recommender Systems (RecSys-2024), located at 18th ACM Conference on Recommender Systems, October 14-18, Bari, Italy, RecSys '24, ISBN 9798400705052, Association for Computing Machinery, 10/2024.
Abstract
In recent decades, Recommender Systems (RS) have undergone significant advancements, particularly in popular domains like movies, music, and product recommendations. Yet, progress has been notably slower in leveraging these systems for personal information management and knowledge assistance. In addition to challenges that complicate the adoption of RS in this domain (such as privacy concerns, heterogeneous recommendation items, and frequent context switching), a significant barrier to progress in this area has been the absence of a standardized benchmark for researchers to evaluate their approaches. In response to this gap, this paper presents a benchmark built upon a publicly available dataset of real-life knowledge work in context (RLKWiC). This benchmark focuses on evaluating context-based entity recommendation, a use case for leveraging RS to support knowledge workers in their daily digital tasks. By providing this benchmark, it is aimed to facilitate and accelerate research efforts in enhancing personal knowledge assistance through RS.
Projects
- 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