This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
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This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
leiyunin/Hybrid-Recommendation-System
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This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
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