The Hot New Releases recommendation algorithm highlights recently launched or popular items, offering users a dynamic and curated selection of products that have gained momentum or are trending, enhancing the discovery of fresh and appealing content.
This Algorithm is a machine learning algorithm designed to provide recommendations for new releases in a headless solution. It uses user preferences to identify and recommend the latest and most popular items that are likely to be of interest to the user.
How does it work?
In Experro, the Hot New Releases recommendation algorithm functions by identifying and showcasing recently launched or trending items, providing users with a dynamic and curated selection of fresh content for enhanced discovery and engagement. The algorithm considers factors such as release date and popularity to present users with the latest and most appealing items.
Supported Rule Types
- Global
- Home Page
- Category
- Product
Use Cases of Merchandising Rules in HNR
- Pinning and slotting works
- Exclude or include works
- Boost, Buy, and Sort will not work for hot new releases
The behavior for logged-in and not-logged-in users is the same.
When should you use this algorithm?
The Hot New Releases Recommendation Algorithm is particularly useful in headless solutions where there is a need to provide users with up-to-date recommendations on new releases. It is suitable for applications such as e-commerce platforms, streaming services, or news aggregators that constantly introduce new content or products.
This algorithm is beneficial for users who want to discover the latest and most popular items in their areas of interest. It helps increase user engagement and satisfaction and encourages exploration of new releases.
Example
Here is an example of how the Hot New Releases algorithm could be used in Experro
- A shopper visits an e-commerce website and views a product page for a chair.
- Experro uses the Hot New Releases algorithm, which generates personalized recommendations for different type of chair releases that are likely to be of interest to the user. The recommendations may include items such as new arrivals, popular styles, or items that are similar to the user's past purchases.
- For instance, if the user frequently buys chairs, the algorithm may recommend new releases of Lounge Chair, Corner Chair, Cushion Chair or other chairs that are in style. If the user has a preference for a particular brand or color, the algorithm can take that into account as well.
- It displays a list of recommended products for the shopper on the product page.
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