# News > Source: https://docs.recombee.com/logics/news > For the complete documentation index, see [llms.txt](/llms.txt). # News Set of fine-tuned News Logics, specifically crafted to enhance recommendation experiences for news platforms, ensuring relevance and timeliness. ## news:daily-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](https://docs.recombee.com/recipes/news/homepage/top-stories) guide for more information. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user) --- ### Parameters editorsPicks Item IDs Array Default: **\[\]** A selection of editorially chosen items that are prepended to the recommended content list, ensuring these highlighted items are seen first by the user. --- publishedTimestamp Property Default: **None** Property indicating the publication date of articles, used to assess content freshness. ## news:personal 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. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user)· [Recommend Items to Item](/api/recommendations#request-recommend-items-to-item)· [Recommend Items to Item Segment](/api/recommendations#request-recommend-items-to-item-segment) ## news:read-next 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](https://docs.recombee.com/recipes/news/article/read-next) guide for more information. --- Applicable to endpoints [Recommend Items to Item](/api/recommendations#request-recommend-items-to-item) ## news:trending 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. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user)· [Recommend Items to Item](/api/recommendations#request-recommend-items-to-item)· [Recommend Items to Item Segment](/api/recommendations#request-recommend-items-to-item-segment) --- ### Parameters excludeAlreadyRead Boolean Default: **true** Excludes articles the user has already read. ## news:popular 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. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user)· [Recommend Items to Item](/api/recommendations#request-recommend-items-to-item)· [Recommend Items to Item Segment](/api/recommendations#request-recommend-items-to-item-segment) --- ### Parameters excludeAlreadyRead Boolean Default: **true** Excludes articles the user has already read. --- timePeriod Duration Default: **14d** The time frame used to measure item popularity. ## news:recent 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](https://docs.recombee.com/recipes/news/homepage/latest-news) guide for more information. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user)· [Recommend Items to Item Segment](/api/recommendations#request-recommend-items-to-item-segment) --- ### Parameters excludeAlreadyRead Boolean Default: **true** Excludes articles the user has already read. --- publishedTimestamp Property Default: **None** Property indicating the publication date of articles, used to assess content freshness. ## news:related 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. --- Applicable to endpoints [Recommend Items to Item](/api/recommendations#request-recommend-items-to-item) --- ### Parameters excludeAlreadyRead Boolean Default: **true** Excludes articles the user has already read. ## news:categories-for-you 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. --- Applicable to endpoints [Recommend Item Segments to User](/api/recommendations#request-recommend-item-segments-to-user) ## news:editors-picks Model ensemble suitable for recommendations of hand-picked editorial content. See the [Scenario Recipe](https://docs.recombee.com/recipes/news/homepage/personalized-editors-picks) guide for more information. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user) --- ### Parameters picks Item IDs Array Default: **\[\]** List of items to be recommended. --- excludeAlreadyRead Boolean Default: **false** Exclude already read articles. --- personalizedReordering Boolean Default: **true** Reorder picked articles in a personalized way for the user. ## news:emailing 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](https://docs.recombee.com/recipes/news/personalized-emailing) guide for more information. --- Applicable to endpoints [Recommend Items to User](/api/recommendations#request-recommend-items-to-user) --- ### Parameters editorsPicks Item IDs Array Default: **\[\]** A selection of editorially chosen items that are prepended to the recommended content list, ensuring these highlighted items are seen first by the user. ## news:articles-from-top-category-for-you 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](https://docs.recombee.com/recipes/news/homepage/personalized-sections-with-reordering) guide for more information. --- Applicable to endpoints [Composite Recommendation](/api/recommendations#composite-recommendation) --- ### Composite Stages For Recommendation Source Result User Item Segment Items Source Endpoint: **Item Segments to User** The Logic returns the most relevant Item Segment (e.g., the top category) for the user from the specified Segmentation. --- Result Endpoint: **Items to Item Segment** The Logic returns the list of the top items within the top Segment. ## news:articles-from-top-segment-for-you 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. --- Applicable to endpoints [Composite Recommendation](/api/recommendations#composite-recommendation) --- ### Composite Stages For Recommendation Source Result User Item Segment Items Source Endpoint: **Item Segments to User** The Logic returns the most relevant Item Segment (e.g., the top category) for the user from the specified Segmentation. --- Result Endpoint: **Items to Item Segment** The Logic returns the list of the top items within the top Segment. ## news:because-you-read 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. --- Applicable to endpoints [Composite Recommendation](/api/recommendations#composite-recommendation) --- ### Composite Stages For Recommendation Source Result User Item Items Source Endpoint: **Items to User** The Logic selects and returns an article that the user has previously read. #### Parameters maxDaysAgo Integer Default: **7** Defines the maximum number of days since the user purchased the item for it to be considered for the "Because" selection. For example, setting maxDaysAgo to 14 will only consider items purchased within the last 14 days. --- Result Endpoint: **Items to Item** The Logic 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 ## news:search Smart full-text search for news articles, with configurable freshness, trendingness, and personalization sliders. --- Applicable to endpoints [Search Items](/api/search#request-search-items) --- ### Parameters allowEmptyQueryFallback Boolean Default: **true** Controls the behavior of the Logic when the `searchQuery` is empty. If set to _true_, the Logic returns personalized recommendations in case of an empty search query. If set to _false_, an empty result list is returned instead. --- freshnessDateProperty Property Default: **None** Name of the item property that stores the article publication date, used for freshness scoring. --- freshnessWeight Double Default: **0.5** Controls how much freshness affects the final ranking. --- trendingnessPeriod Duration Default: **1d** Time window from which interaction data is used to compute trendingness. --- trendingnessWeight Double Default: **0.3** Controls how much trendingness affects the final ranking. --- personalizationWeight Double Default: **0.2** Controls how much personalization affects the final ranking.