COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Predict if a user will buy the product based on all the other products they bought
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Classify the products to categories to recommend the user based on the most common category bought
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Predict the next item that user will buy based on every the products all users bought
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Predict the item the user will buy based on other users’ purchase history and the product ratings and reviews
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Detailed explanation-1: -Recommendation engines basically are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. Or in simple terms, they are nothing but an automated form of a “shop counter guy”. You ask him for a product.
Detailed explanation-2: -To build a system that can automatically recommend items to users based on the preferences of other users, the first step is to find similar users or items. The second step is to predict the ratings of the items that are not yet rated by a user.
Detailed explanation-3: -What are Personalized Product Recommendations? Personalized product recommendations are when a site shows a selection of product recommendations that’s unique to the individual visitor, based on their behaviors and profile. This is almost always based on a machine learning algorithm.
Detailed explanation-4: -The most important thing is to establish exactly how the products you’re recommending are relevant to your customer. On the homepage, that might mean you’re showing your “Best Selling Items”. On product pages, you can show products ‘Frequently bought together’ to encourage higher average cart values.