News

Model ensemble optimized for high-visibility placements.

It ensures users stay connected to the most significant news stories while maintaining a personalized experience based on their past behavior.

It caters to frequent visitors by consistently offering fresh, relevant content.

See the Scenario Recipe guide for more information.

Model ensemble providing individualized content suggestions, carefully selecting the best choices from the catalog to match each user's specific interests.

It is particularly suitable for the For You scenarios, where personalized recommendations are essential.

Model ensemble recommending personalized articles most likely to engage users after they finish reading the current article.

It considers the content of the current article and analyzes both recent and long-term user behavior to encourage continued reading and extend visit duration.

Suitable for Continue Reading or You Might Also Like scenarios.

See the Scenario Recipe guide for more information.

Model ensemble recommending news articles that are currently trending, providing users with content that is gaining widespread interest and should not be missed.

Suitable for the Trending Articles scenario.

Model ensemble recommending articles that are highly popular within a specified time period.

It is ideal for Popular Articles scenario, allowing users to see what others find interesting.

Model ensemble showcasing the most recently published articles, ensuring users stay updated with the latest content.

Suitable for the Latest News scenario.

See the Scenario Recipe guide for more information.

Model ensemble recommending articles that are related to the current article, providing users with a seamless continuation of their reading experience on a given topic.

Suitable for the Related Articles scenario.

Model ensemble recommending categories relevant to the user based on their previously read articles.

Suitable, for example, for personalized re-ordering of sections on the homepage.

Assumes the scenario is configured with an Item Segmentation representing article categories.

Model ensemble suitable for recommendations of hand-picked editorial content.

See the Scenario Recipe guide for more information.

The news:emailing Logic is designed to provide a personalized selection of articles for email newsletters.

This Logic combines:

• Personalized recommendations based on the user's reading history and preferences.
• Key unread articles to ensure the user stays informed about the most important news they haven’t seen yet.

To maintain content freshness, it is advisable to apply a Filter that limits the selection to recently published articles, depending on the email frequency. For example, for a daily digest, you can filter for articles published within the last 24 hours.

See the Scenario Recipe guide for more information.

This Composite Logic provides a comprehensive recommendation in a single response consisting of:

• Top Segment for the User: It identifies and returns the most relevant Item Segment (e.g., the top category) for the user from the specified Segmentation.
• Top Items from the Segment: Along with the Segment, it also returns a list of the top items within that Segment.

This Logic is ideal for recommending a section and its content together, making it particularly useful for personalized homepage sections.

If you want to display multiple distinct sections in a personalized order on your homepage, you can achieve this by making multiple Composite Recommendation requests within a single Batch call.

For more specific use cases, such as "Videos From The Top Genre For You" or "Products from the Top Category For You," there are specialized Logics available.

See the Scenario Recipe guide for more information.

This Composite Logic delivers personalized news article recommendations in a single response:

• Top Segment for the User: It identifies and returns the most relevant Segment for the user. Depending on the selected Item Segmentation, this Segment could be based on attributes such as authors or tags.
• Top Articles from the Segment: Along with the top Segment, it returns a list of the top articles associated with that Segment.

This Logic is ideal for recommending both a Segment (e.g., a favorite author or popular tag) and its associated articles. For example, it can be used for scenarios like "Top Articles from Your Favorite Author".

If you wish to display multiple distinct Segments in a personalized order, you can achieve this by making multiple Composite Recommendation requests within a single Batch call.

This Composite Logic delivers personalized news article recommendations based on the user's previous reading activity, all within a single response:

• Article the User Read: It selects and returns an article that the user has previously read.
• Top Related Articles: Along with the read article, it returns a list of top related articles focused on the same or similar topics, offering follow-up content or deeper insights into the subject the user has already shown interest in.

This Logic is ideal for scenarios like “Because You Read …,” where recommendations are tailored to the user’s reading history. It can recommend follow-up articles, related pieces, or additional articles on similar subjects.

Smart full-text search for news articles, with configurable freshness, trendingness, and personalization sliders.