Work

Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data

Year: 2024

Type: conference-paper

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.

Source: ACM Conference on Recommender Systems (RecSys)

DOI: 10.1145/3640457.3688068

Authors: Mahta Bakhshizadeh, Heiko Maus, Andreas Dengel

Institutions: German Research Centre for Artificial Intelligence, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung

Awards: Nachhaltige evolutionäre Unternehmensgedächtnisse: Methoden und Effekte von Managed Forgetting für die administrative Wissensarbeit, G8254455972