Problem statement: Online learning platforms provide a wealth of educational resources, but students may struggle to find the content that is most relevant to their learning needs. A recommender system based on collaborative filtering could provide personalized recommendations for students.
Conduct a literature review of existing research on recommender systems and collaborative filtering.
Develop a dataset of student interactions with online learning resources, such as course materials and quizzes.
Select an appropriate collaborative filtering algorithm, such as user-based or item-based filtering, and tune the hyperparameters using cross-validation.
Implement the recommender system using Python and a machine learning library such as scikit-learn or TensorFlow.
Evaluate the performance of the recommender system using metrics such as mean average precision and recall.
Write a report summarizing your research, methodology, and findings.
Problem statement: Online learning platforms provide a wealth of educational resources, but students may struggle to find the content that is most relevant to their learning needs. A recommender system based on collaborative filtering could provide personalized recommendations for students.
Conduct a literature review of existing research on recommender systems and collaborative filtering. Develop a dataset of student interactions with online learning resources, such as course materials and quizzes. Select an appropriate collaborative filtering algorithm, such as user-based or item-based filtering, and tune the hyperparameters using cross-validation. Implement the recommender system using Python and a machine learning library such as scikit-learn or TensorFlow. Evaluate the performance of the recommender system using metrics such as mean average precision and recall. Write a report summarizing your research, methodology, and findings.