The collaborative filtering algorithm is the Popular Items AI recommendation algorithm used for personalization. It is a technique that recommends items to users based on their past behavior and preferences, as well as the behavior and preferences of other users with similar profiles.
How does it work?
The Popular Products Recommendation Algorithm identifies and recommends items that have gained widespread popularity among users, taking into account factors such as high engagement, views, and frequent purchases. By leveraging the collective preferences of the user base, this algorithm emphasizes products that resonate well with the audience, aiming to enhance user satisfaction and drive conversions. It is particularly effective for showcasing trending or top-rated items to users, contributing to a dynamic and engaging user experience.
Supported Rule Types
- Global
- Home Page
- Category
- Product
The behavior for logged-in and not-logged-in users is the same.
When should you use this algorithm?
Use the Popular Products Recommendation Algorithm in Experro when aiming to showcase widely liked or trending items to users, enhancing engagement and driving conversions. This algorithm is well-suited for e-commerce platforms and content-driven websites, where promoting popular or highly-rated products aligns with the goal of providing a dynamic and appealing user experience.
Example
Here is an example of how the Popular Products algorithm could be used in Experro:
- A shopper visits an e-commerce website and views a product page for a designer chair.
- Experro uses the Popular Products algorithm, which will analyze your past behavior and preferences, including your purchase history and ratings, to recommend other designer chairs that other users with similar profiles have enjoyed.
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It displays a list of popular products for the shopper on the product page.
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