An Efficient Hybrid Recommender System for e-Learning Based on Cloud Content in Educational Web Services

Abstract

In this article, we present a novel method for multimodal learning using Siamese networks to recommend appropriate educational content on e-learning platforms. One of the main challenges in current recommendation systems is their inability to effectively personalize content based on the unique needs and preferences of individual learners. Existing methods often struggle to capture long-term dependencies and intricate patterns in user behavior, leading to irrelevant or inadequate content suggestions. To address this, our approach utilizes two residual Siamese networks based on Long Short-Term Memory (LSTM) and Recurrent Convolutional Neural Networks (RCNN). This hybrid model effectively captures both sequential and contextual information, leveraging LSTM's strength in handling long-term dependencies and RCNN's capability to extract local features through convolutional operations. By analyzing complex patterns within the data, our method significantly enhances recommendation accuracy, considering both temporal sequences and contextual relationships. The Siamese network encodes user and item data into a high-dimensional feature space, positioning similar users and items closer together. The residual connections allow the model to capture both low-level and high-level features, leading to richer representations. Extensive experiments on real-world e-learning datasets demonstrate the superiority of our method over traditional recommendation techniques, evaluated through metrics such as precision, recall, and accuracy. The results show that our approach not only improves recommendation accuracy but also enhances the diversity and relevance of suggested content, offering more personalized learning experiences that cater to the individual needs and preferences of learners.