Abstract：Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering Purpose The purpose of this paper is to provide adaptive access to learning resources in the digital libraryDesignmethodologyapproach A novel method using ontology-based multi-attribute collaborative filtering is proposedDigital 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 electronicallyTo satisfy usersinformation needsa humongous amount of newly created information is published electronically in digital librariesWhile search applications are improvingit is still difficult for the majority of users to find relevant informationFor better servicethe framework should also be able to adapt queries to search domains and target learnersFindings This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital librariesTo facilitate a personalized digital learning environmentthe authors propose a novel method using ontology-supported collaborative filteringCFrecommendation systemThe objective is to provide adaptive access to learning resources in the digital libraryThe proposed model is based on user-based CF which suggests learning resources for students based on their course registrationpreferences for topics and digital librariesUsing ontological framework knowledge for semantic similarity and considering multiple attributes apart from learnerspreferences for the learning resources improve the accuracy of the proposed modelResearch limitationsimplications The results of this work majorly rely on the developed ontologyMore experiments are to be conducted with other domain ontologiesPractical implications The proposed approach is integrated into Nucleusa Learning Management System results are of interest to learnersacademiciansresearchers and developers of digital librariesThis work also provides insights into the ontology for e-learning to improve personalized learning environmentsOriginalityvalue This paper computes learner similarity and learning resources similarity based on ontological knowledgefeedback and ratings on the learning resourcesThe predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CFRetrieval and indexingThe core of this article is to improve the efficiency and accuracy of finding related learning resources in digital libraries This article proposes a new method to provide adaptive access to the learning resources in digital libraries with multi-attribute coordinated filteringThis involves the prediction and recommendation of personalized learning resources to predict and recommend personalized learning resources by using user preferencesregistered coursesand their feedback and scores of learning resourcesData exploration technology is used to find other users and learning resources similar to usersand generate recommendations based on these similarity