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To build a recommendation system to recommend products to customers based on the their previous ratings for other products
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Go to fileTo build a recommendation system to recommend products to customers based on the their previous ratings for other products
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.
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 ( ignoring this column for our experiment)
●Exploratory Data Analysis ●Creating a Recommendation system using real data ●Collaborative filtering
1.Read and explore the given dataset. 2.Take a subset of the dataset to make it less sparse/ denser. 3.Split the data randomly into train and test dataset. 4.Build Popularity Recommender model. 5.Build Collaborative Filtering model. 6.Evaluate both the models. 7.Get top -K ( K = 5) recommendations. Since our goal is to recommend new products foreach user based on his/her habits, we will recommend 5 new products. 8.Summarise your insights.
To build a recommendation system to recommend products to customers based on the their previous ratings for other products