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• W4313444316 abstract "With the rapid increase in Social Web applications, a significant amount of research has been dedicated to the analysis and development of a personalized recommendation using social tags. The social tags will allow the users to select their preferences and object of interest. Different machine learning and neural networks are applied widely to address the problem of tag ambiguity. However, conventional machine learning algorithms require additional training models to train recommendation models for processing large scale data. Hence they are considered to be inappropriate for generating personalized recommendations in learning systems. This research presents an LSTM-CNN-based approach for generating appropriate learning recommendations. The paper presents an ontology-based similarity metric to overcome the issue of tag ambiguity without requiring any additional training model. The proposed approach semantically examines and quantifies the similarities between user and item profiles by matching the concepts of tags. The effectiveness of this approach was validated using different performance metrics and results validate the performance of the proposed approach." @default.
• W4313444316 created "2023-01-06" @default.
• W4313444316 creator A5002368874 @default.
• W4313444316 creator A5003144405 @default.
• W4313444316 date "2023-01-01" @default.
• W4313444316 modified "2023-10-16" @default.
• W4313444316 title "PLRec: An Efficient Approach Towards E-Learning Recommendation Using LSTM-CNN Technique" @default.
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• W4313444316 doi "https://doi.org/10.1007/978-981-19-2358-6_50" @default.
• W4313444316 hasPublicationYear "2023" @default.
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