Editorial on the Research Topic: Natural Language Processing for Recommender Systems

Alfred Krzywicki ^1* Michael Bain ² Wayne Wobcke ²

• ¹ University of Adelaide, Adelaide, Australia
• ² University of New South Wales, Kensington, New South Wales, Australia

automating competency score prediction to address the inefficiencies and biases inherent in manual assessment. This work aligns with performance modelling approaches, such as Thai-Nghe et al. (2010) in education-focused systems, but extends them into project management with a multi-modal and NLP-enhanced framework. Unlike existing models like Shahhosseini and Sebt (2011), which use fuzzy logic to assign competencies in construction projects, Jemal et al. incorporate robust recommendation techniques and NLP embeddings to enhance prediction accuracy.The study's focus on multi-modal data integration sets it apart from traditional frameworks (e.g., Dainty et al., 2005), while its use of advanced NLP tools contrasts with simpler regressionbased methods. By addressing cold-start challenges for new users and competencies, this research makes a significant contribution to both recommender systems and competencybased evaluation. Discussion. These studies share several common themes that highlight key priorities and methods in using NLP for recommender systems. First, all emphasize the importance of context. Whether it's understanding data, explaining recommendations, or evaluating competencies, context helps make recommendations more relevant and useful.Second, the studies use advanced NLP techniques to analyse and transform text data. For example, Dietz et al. use ranking methods, while Bhuvaneswari and Varalakshmi rely on hybrid training models. These approaches show how NLP not only supports but also drives solutions for specific challenges, delivering clear performance improvements.Third, there's a focus on innovation through combining different methods and data types. Jemal et al. use a multi-modal framework, while Zhang et al. explore how different explanation styles affect user satisfaction. These examples show the growing need for more complex systems that can handle diverse requirements, which aligns with the trend of using hybrid models and multimodal data processing to improve recommender systems.