Video

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

See the Scenario Recipe guide for more information.

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.

See the Scenario Recipe guide for more information.

Model ensemble suitable for recommending favorite segments (genres, actors, ...) for a specific user.

A particular segmentation must exist beforehand.

See the Scenario Recipe guide for more information.

Model ensemble suitable for recommending globally popular items.

See the Scenario Recipe guide for more information.

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

See the Scenario Recipe guide for more information.

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

See the Scenario Recipe guide for more information.

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

See the Scenario Recipe guide for more information.

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

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

See the Scenario Recipe guide for more information.

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.

See the Scenario Recipe guide for more information.

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

• Top Genre for the User: It identifies and returns the most relevant genre or category for the user, based on their viewing history and preferences.
• Top Videos from the Genre: Along with the top genre, it also returns a list of the top videos within that genre or category.

This Logic is ideal for recommending both a genre and its associated videos together, making it particularly useful for personalized sections on the homepages of video streaming platforms.

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

It is expected that the Item Segmentation selected in the configuration represents the genres or categories of the videos.

See the Scenario Recipe guide for more information.

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

• Top Segment for the User: It identifies and returns the most relevant Segment for the user. Depending on the selected Item Segmentation, this could be a Director, Theme, Tag, Actor, etc.
• Top Videos from the Segment: Along with the top Segment, it also returns a list of the top videos associated with that Segment.

Based on the selected Item Segmentation, this Logic can be used for rows such as “Your Favorite Director … And Their Movies” or “Videos About Your Favorite Topic …”.

If you wish to display multiple rows for distinct top 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 recommendations based on a user's previous watching activity, all within a single response consisting of:

• Video the User Watched: It selects and returns a video that the user has recently watched.
• Top Related Videos: Along with the watched video, it also returns a list of top videos related to that specific video, offering content the user is likely to be interested in.

This Logic is ideal for scenarios like “Because You Watched …,” where recommendations are tailored to the user’s recent watching history.

The watched video is determined based on View Portion interactions, and you can refine this by specifying the maxDaysAgo and minWatchedPercentage parameters to define which View Portions are considered. If no such View Portion exists, an empty recommendation is returned.

If you want to display multiple sets of related videos based on different watched content, you can achieve this by making multiple Composite Recommendation requests within a single Batch call.

See the Scenario Recipe guide for more information.