A highly sophisticated, tested, robust and procedural recommender.
Recommendation systems: Rank based and Collaborative Filtering (Neighbor-based and Model-based) approach to recommend articles based on user-article interactions.
The project's goal is to create diverse recommendation systems that predict user-item ratings
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.
A highly sophisticated, tested, robust and procedural recommender.
The assignment comprises two main tasks: implementing LSH to identify similar businesses based on user ratings and developing various collaborative filtering recommendation systems to predict user ratings for businesses.
Model-Based and Memory-Based collaborative filtering recommendation system on retail data.
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