With the rise of social media, individuals face challenges in decision-making due to the
abundance of options available. Recommender Systems (RSs) leverage Artificial …

During the last decades, recommender systems are becoming quite popular since they
provide great assistance to users on social networks and library websites. Unfortunately, the …

Abstract The Hayes-Wheelwright Product-Process Matrix (PPM) has historically been a
pivotal tool in manufacturing, guiding firms to strategically align product volume and variety …

This paper addresses the challenges of data sparsity and personalization limitations
inherent in current recommendation systems when processing extensive academic paper …

Abstract Recommendation (recommender) systems (RS) have played a significant role in
both research and industry in recent years. In the area of academia, there is a need to help …

With the rapid growth of research publications, it has become time-consuming and
cumbersome for researchers to find research papers relevant to their research. Research …

Pre-trained models learn general representations from large datsets which can be fine-
turned for specific tasks to significantly reduce training time. Pre-trained models like …

The large number of scientific publications around the world is increasing at a rate of
approximately 4%–5% per year. This fact has resulted in the need for tools that deal with …

Abstract Aspect-Controllable Related Papers Recommendation (ACRPR) aims to satisfy
users' fine-grained needs for specific aspects when finding related papers. Existing …

Purpose This paper aims to curate open research knowledge graph (ORKG) with papers
related to ontology learning and define an approach using ORKG as a computer-assisted …