Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a persona More
Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a personalized environment to deliver accurate recommendations. In the technology enhanced learning (TEL) field and in particular resource-based learning, recommendation systems have attracted growing interest. Specially, they are an important module of Adaptive Educational Systems that deliver the appropriate learning objects to their users. Gray-sheep users are a challenge in these systems. They have a little similarity with their peers. So the recommendations to others are not suitable for them. To overcome this problem, we propose the idea of accommodating the user’s learning style to web page features. The user's learning style will be computed according to Felder-Silverman theory. On the other hands, the necessary implicit and explicit meta data will be extracted from OCW web pages. By matching the extracted information, the system delivers the appropriate educational links to user. The user can compare the proposed links, based of our algorithm, to the output of Lucene algorithm. A user’s opinion about every web page as a recommended result would be submitted to the system. The system uses a learning automata algorithm and user’s feedback to deliver best recommendations. The system has been evaluated by a group of engineering students to evaluate its accuracy. Results show that the proposed method outperforms the general search algorithm. This system can be used at formal and informal learning and educational environments for Resource-based learning.
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Using the context and order of words in sentence can lead to its better understanding and comprehension. Pre-trained language models have recently achieved great success in natural language processing. Among these models, The BERT algorithm has been increasingly popular More
Using the context and order of words in sentence can lead to its better understanding and comprehension. Pre-trained language models have recently achieved great success in natural language processing. Among these models, The BERT algorithm has been increasingly popular. This problem has not been investigated in Persian language and considered as a challenge in Persian web domain. In this article, the embedding of Persian words forming a sentence was investigated using the BERT algorithm. In the proposed approach, a model was trained based on the Persian web dataset, and the final model was produced with two stages of fine-tuning the model with different architectures. Finally, the features of the model were extracted and evaluated in document ranking. The results obtained from this model are improved compared to results obtained from other investigated models in terms of accuracy compared to the multilingual BERT model by at least one percent. Also, applying the fine-tuning process with our proposed structure on other existing models has resulted in the improvement of the model and embedding accuracy after each fine-tuning process. This process will improve result in around 5% accuracy of the Persian web ranking.
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