Real-World Applications and Impact of Hybrid Scalable Researcher Recommendation Systems

EasyChair Preprint 14070

Abstract

Hybrid scalable researcher recommendation systems have emerged as pivotal tools in the academic and scientific domains, revolutionizing the way researchers discover relevant information and collaborate with their peers. This paper explores the real-world applications and impact of these systems. Various applications, such as academic research platforms, funding agencies, conference and journal submission systems, and collaborative research platforms, are discussed. The impact of hybrid scalable researcher recommendation systems includes enhanced research productivity, increased visibility and recognition for researchers, efficient allocation of resources, and the advancement of scientific knowledge. However, challenges related to data quality, privacy, personalization, and algorithm evaluation need to be addressed. Understanding the potential of these systems and their impact on the academic and scientific communities is crucial for fostering innovation and collaboration in research. Future developments hold promise for further improving these systems and maximizing their benefits.

Keyphrases: Efficient Resource Allocation, hybrid scalable, researcher recommendation systems, scientific knowledge advancement

@booklet{EasyChair:14070,
  author    = {Kayode Sheriffdeen and Toheeb Olaoye},
  title     = {Real-World Applications and Impact of Hybrid Scalable Researcher Recommendation Systems},
  howpublished = {EasyChair Preprint 14070},
  year      = {EasyChair, 2024}}