Improving a recommender system
Budget: $30 – $250 USD
I am looking for someone who can improve the code written with Pycharm. It is supposed to be a collaborative filtering recommender system. It has to answer questions like these:
What are the most effective similarity metrics for user-based collaborative filtering on Amazon dataset? Perform experiments using various similarity metrics (such as cosine similarity, Pearson correlation coefficient, Jaccard similarity, etc.)
How effective is collaborative filtering for recommending products to users based on their past behavior and the behavior of other users with similar interests? Evaluation metrics, such as mean absolute error, root mean squared error, precision, recall, and F1-score
- Experience with collaborative filtering recommender system
- Ability to implement new features and improve existing ones
- Familiarity with machine learning algorithms
If you believe you have the necessary skills and experience, we would love to hear from you!
What are the most effective similarity metrics for user-based collaborative filtering on Amazon dataset? Perform experiments using various similarity metrics (such as cosine similarity, Pearson correlation coefficient, Jaccard similarity, etc.)
How effective is collaborative filtering for recommending products to users based on their past behavior and the behavior of other users with similar interests? Evaluation metrics, such as mean absolute error, root mean squared error, precision, recall, and F1-score
- Experience with collaborative filtering recommender system
- Ability to implement new features and improve existing ones
- Familiarity with machine learning algorithms
If you believe you have the necessary skills and experience, we would love to hear from you!