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Popularity based Recommendation System, Content Based Recommendation System, Cosine Similarity

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Shivang-Shrivastav/Recommendation-Systems

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Recommendation-Systems

Data Description: Amazon Reviews data (data source) The repository has several datasets. For this case study, we are using the Electronics dataset.

Domain: E-commerce

Context: Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time.

Attribute Information: ● userId : Every user identified with a unique id ● productId : Every product identified with a unique id ● Rating : Rating of the corresponding product by the corresponding user ● timestamp : Time of the rating ( ignore this column for this exercise) Learning Outcomes: ● Exploratory Data Analysis ● Creating a Recommendation system using real data ● Collaborative filtering

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Popularity based Recommendation System, Content Based Recommendation System, Cosine Similarity

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