https://www.kaggle.com/code/shiblinomani/recommender-system-using-movielens-20m-dataset
https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset
Definition of Recommender System: A recommender system is a type of information filtering system that predicts preferences or ratings a user would give to a particular item. It analyzes user behavior and item characteristics to provide personalized recommendations.
Why it is Useful: Recommender systems are valuable for users and businesses alike:
Users: They help users discover new items of interest based on their preferences, leading to a more personalized and satisfying experience. Businesses: Recommender systems can increase user engagement, retention, and sales by providing targeted recommendations, leading to improved customer satisfaction and revenue.
Types of Recommender Systems:
Content-Based Recommender Systems: T hese recommend items similar to those a user has liked in the past. They analyze item features and user profiles to make recommendations.
Example: A music streaming service recommending songs based on the genre or artist a user frequently listens to.
Collaborative Filtering Recommender Systems:
a. User-Based Collaborative Filtering: Recommends items based on the preferences of similar users. b. Item-Based Collaborative Filtering: Recommends items similar to those a user has liked in the past. Example: A movie streaming platform suggesting movies to a user based on the ratings and preferences of users with similar tastes.
Hybrid Recommender Systems: Combine multiple recommendation techniques to provide more accurate and diverse recommendations.
Example: A hybrid system that combines collaborative filtering with content-based filtering to recommend movies based on both user preferences and movie features. Cosine Similarity with Simple Example: Cosine similarity is a measure used to determine the similarity between two vectors in a multidimensional space. It calculates the cosine of the angle between the vectors, indicating their directional similarity regardless of their magnitudes.
📊 Context: The dataset encompasses user ratings and free-text tagging from MovieLens, a movie recommendation platform. It comprises 20,002,263 ratings and 465,564 tag applications across 27,278 movies.
👥 User Information: These data originated from 138,493 users randomly selected for inclusion. All users in the dataset have rated a minimum of 20 movies.
📅 Timeline: The dataset spans user activities from January 09, 1995, to March 31, 2015. The dataset itself was generated on October 17, 2016.
🎬 MovieLens Universe: The dataset provides insights into user preferences, offering a comprehensive view of movie ratings and tags, forming the basis for MovieLens recommendation services.