Recommendation Logics

Default model ensemble aiming to work well in most common use cases. It is an ensemble of collaborative filtering and content-based recommendation, backed by popularity-based models when there's not enough data in the given context. The ensemble is being continuously optimized by AI so it automatically adapts to incoming data.

Universal model ensemble suitable for putting recommendations on the homepage / welcome-screen scenarios.

Universal model ensemble suitable for user-based recommendations reflecting the personal tastes of the current user.

Universal model ensemble suitable for either recommending items which are similar to the given item (in case of Items-to-Item recommendation), or recommending users who are similar to the given user (in case of Users-to-User recommendation).

Universal model suitable for recommending globally popular items, taking into account all interaction types. More important actions (purchases, cart-additions) have higher weight for computing the popularity than less important actions (detail-views).

Universal model ensemble suitable for recommending globally popular segments.

Model returning the list of recently viewed items for a particular user.

Model recommending similar items based on visual similarity (using deep-learning neural networks to process the images provided through the image properties). While the image similarity can take part in other logics as well, in recombee:visually-similar, it is explicitly used as the primary model. Image processing may take some time. Therefore, changes in image item properties do not take effect in real-time. The functionality is still in beta mode.

Model recommending similar items based on item properties (numerical values, sets, and texts). While property values can take part in other logics, in recombee:similar-properties, they are explicitly used as the primary data source for recommending similar items or uses. Various machine-learning methods, including NLP deep-learning networks that process unstructured text descriptions, are used to compute the similarities.

Model ensemble suitable for recommending items in periodic e-mailing campaigns. It contains fine-tuned rotation settings so that the set of recommended items for a particular user changes with each e-mail sent. By default, rotation only takes into account recommendations (e-mails) at most 14 days old. Should a longer time period be needed for your use case, kindly contact support@recombee.com.

Model suitable for recommending items ordered by a ReQL expression given as a booster

Recommends items that are contextually related to the Item Segment and should be of interest to the user. Items may belong to the Segment or be outside it if they are strongly relevant.

Consider using recombee:personal-from-segment if you want recommendations strictly limited to items within the Segment.

Examples:

• News: For the "Climate Change" Segment, recommend articles about renewable energy (inside the Segment) or related topics like electric vehicles (outside the Segment).
• Music: For the "Jazz" Segment, recommend jazz-related songs, including classic tracks (inside the Segment) or modern experimental pieces (outside the Segment).
• E-commerce: For the "Digital Cameras" Segment, recommend products like digital cameras (inside the Segment) or accessories like tripods, lenses, or lighting kits (outside the Segment).
• Video Streaming: For the "Science Fiction" Segment, recommend sci-fi movies (inside the Segment) or related dystopian thrillers (outside the Segment).

Recommends items from the Item Segment, tailored to the user's preferences.

Consider using recombee:related-to-segment if you don't need all the recommended items to belong to the Segment.

Examples:

• News: For the "Environmental News" Segment, recommend articles on climate policy or renewable energy that align with the user’s reading history.
• Music: For the "Jazz" Segment, recommend jazz songs matching the user’s listening habits.
• E-commerce: For the "Digital Cameras" Segment, recommend digital cameras the user is most likely to be interested in, based on their browsing and purchase history.
• Video Streaming: For the "Science Fiction" Segment, recommend sci-fi movies that match the user’s preferences.

Recommends the most popular items within the Item Segment, based on overall engagement. Use this Logic to highlight trending or widely appealing items.

Examples:

• News: Most-read articles from the "World Politics" Segment.
• Music: Most-streamed tracks from the "Rock" Segment.
• E-commerce: Best-selling smartphones in the "Smartphones" Segment.
• Video Streaming: Most-watched action movies in the "Action Movies" Segment.

Smart personalized fulltext search which takes into account both the search query and the personal preferences of the user.

Non-personalized fulltext search, based purely on how well the search query is matched.

Smart personalized fulltext search which takes into account both the search query and the personal preferences of the user.

Non-personalized fulltext search, based purely on how well the search query is matched.

Semantic search utilizes a large language model (LLM) to improve search accuracy by understanding the intent and context behind user queries.

Instead of relying solely on keyword matches, the system comprehends the semantic meaning of queries and retrieves results that align with the user's intentions. This advanced Logic delivers more accurate, context-aware search results, even when the query terms differ from the content in the database.

This Logic can be enabled upon request for Recombee Premium customers.

Model ensemble suitable for recommending products on homepage/welcome page in E-Commerce and online retail systems.

Model ensemble suitable for recommending similar products (alternatives) to a source (currently viewed) product.

Model ensemble suitable for recommending compatible/complementary products. In user-based case, the products are compatible with those that the user has already put to the shopping cart. In item-based case, they are compatible with the source (currently viewed) item.

Model suitable for recommending globally most purchased items.

Recommends users with similar purchasing behavior as the source user.

Model ensemble suitable for recommending partially watched movies or episodes, or the next episode from a watched series.

Model ensemble suitable for recommending personalized rows on the homepage or browse pages. Rows with specific content (e.g. award-winning movies) can be set up by applying filters.

Model ensemble suitable for recommending favorite segments (genres, actors, ...) for a specific user. A particular segmentation must exist beforehand.

Model ensemble suitable for recommending globally popular items.

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

Model ensemble suitable for recommending similar assets on the asset detail page.

Model ensemble suitable for end-of-playback recommendations embedded in a video player.

Model ensemble suitable for reverse-chronological listing episodes of a particular series.

Smart personalized full-text search suitable for searching movies, series, and episodes.

Smart personalized full-text search suitable for searching segments (genres, actors, ...). A particular segmentation must exist beforehand.

Model ensemble suitable for sending personalized emails with personalized content for the user. It contains fine-tuned rotation settings so that the set of recommended assets for a particular user changes with each e-mail sent.

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.

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.

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.

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.

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.

Model ensemble suitable for recommending products on homepage/welcome page of a Classified advertising site.

Model ensemble suitable for recommending picked ads right for the given user. Can be used on different places, such as in-category recommendation when augmented with appropriate ReQL filter.

Model ensemble suitable for recommending ads that are similar to the one which is currently viewed.