Gaurav-Pande/Recommendation_systems
Auto encoders based recommendation system
This project helps businesses offer personalized product or content suggestions to their customers. By taking in user activity data like clicks or ratings, it predicts which items a user will like and generates a ranked list of the top 10 recommendations. This is useful for product managers, e-commerce specialists, and content curators looking to enhance user engagement and sales.
No commits in the last 6 months.
Use this if you need to build a recommendation engine that can suggest items to both new users (cold start) and existing users based on their past interactions.
Not ideal if you have a massive dataset of millions of users and products, or if you require an API-ready solution for immediate deployment.
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Language
Python
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Last pushed
Aug 08, 2020
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