This project utilizes several recommendation techniques to provide effective suggestions. Popularity-based recommendations are implemented using Bayesian averages to rank items. Content-based recommendations leverage cosine similarity to suggest items similar to a user's preferences. Additionally, collaborative filtering is implemented using the Surprise library, incorporating both user-based and item-based approaches to enhance personalization.
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razamehar/recommender_system
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