Paper Type
Short
Paper Number
PACIS2025-1401
Description
The web of information is expanding rapidly, making it difficult for individuals to identify and retrieve personalized and context-specific information through traditional methods such as keyword- based search. Recommender systems are on the rise to fill this gap, especially for domains such as e-commerce. However, there are limited studies exploring recommendation systems for expert and specialty needs such as finding research grant opportunities or job vacancies in a specific field. Key reasons for this are lack of a large volume of high-quality homogeneous data to develop such systems, and unique needs of individuals that are difficult to abstract. In this paper, we propose a recommendation system that leverages Generative AI and knowledge graphs to identify suitable research grants for academic researchers. Using continuously available web-based data, we develop and evaluate a case study to demonstrate the system's practical application and effectiveness.
Recommended Citation
Alotaibi, Naif; Bandara, Madhushi; Saberi, Morteza; and Hussain, Farookh Khadeer, "Expert Opportunity Recommendation Framework for Unstructured Web Data Using Generative AI and Knowledge Graphs" (2025). PACIS 2025 Proceedings. 27.
https://aisel.aisnet.org/pacis2025/aiandml/aiandml/27
Expert Opportunity Recommendation Framework for Unstructured Web Data Using Generative AI and Knowledge Graphs
The web of information is expanding rapidly, making it difficult for individuals to identify and retrieve personalized and context-specific information through traditional methods such as keyword- based search. Recommender systems are on the rise to fill this gap, especially for domains such as e-commerce. However, there are limited studies exploring recommendation systems for expert and specialty needs such as finding research grant opportunities or job vacancies in a specific field. Key reasons for this are lack of a large volume of high-quality homogeneous data to develop such systems, and unique needs of individuals that are difficult to abstract. In this paper, we propose a recommendation system that leverages Generative AI and knowledge graphs to identify suitable research grants for academic researchers. Using continuously available web-based data, we develop and evaluate a case study to demonstrate the system's practical application and effectiveness.
Comments
AI ML