Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering

Abstract

PurposeThe purpose of this paper is to provide adaptive access to learning resources in the digital library.Design/methodology/approachA novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.FindingsThis paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.Research limitations/implicationsThe results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.Practical implicationsThe proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.Originality/valueThis paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

Mapping the research landscape of recommender systems for digital libraries

“…However, with the increasing complexity of data and the growing availability of multilingual content in digital libraries (Stiller et al, 2019), there arises a critical need for RS to address diverse language preferences among users. This necessitates the importance of integrating semantics and ontologies into RS (Senthil Kumaran & Latha, 2023), not only to offer more accurate recommendations but also to enhance user engagement and satisfaction by tailoring to their linguistic preferences. There exists a significant potential to implement natural language processing within RS to navigate the complexity of multilingual content and accommodate user interactions from diverse linguistic backgrounds.…”

“…Recent advancements have been made in RS within digital libraries based on user preferences utilizing the Latent Dirichlet Allocation model to recommend items (Zhao, 2021), ontology-based multi-attribute collaborative filtering RS to enhance the access to learning resources within digital libraries (Senthil Kumaran & Latha, 2023). Liu (2021) proposed a personalized RS using SOM neural networks to enhance the web access behavior of users for UDL.…”

Editorial: Special selection on current bibliometrics and reviews

“…Hot topics include machine learning, large datasets, deep learning and high-level languages. Readers may also be interested in reading recent articles on AI library service innovation (Okunlaya et al , 2022), patterns of AI policies (Wang et al , 2023a), intelligent recommendations (Bokharaei Nia et al , 2023; Nawaz et al , 2023; Senthil Kumaran and Latha, 2023; Shi et al , 2023), library robotic process automation (Basumatary et al , 2023; Chin and Chiu, 2023; Lin et al , 2022; Wójcik, 2023), smart libraries (Khan et al , 2023; Wang, 2023b), AI awareness in libraries (Harisanty et al , 2022; Yoon et al , 2022), AI in library repositories (Nugroho et al , 2023) and ChatGPT (Afjal, 2023; Bin-Hady et al , 2023). Further, readers may be interested in our special section on smart cities (Chang and Abdel-Basset, 2022).…”

Editorial: Special selection on contemporary bibliometric analytics

“…They showed the key factors and challenges in developing SDLs. Readers may be interested in a recent article on enhancing adaptive access to digital libraries using ontology-supported collaborative filtering (Senthil Kumaran and Latha, 2023).…”