Recommendations allow users to present unique AI-powered item recommendations through several channels in order to promote items and encourage customers to make a purchase. We use the AI engine to acquire information from your website and analyze large portions of data which is mainly customers' activity (visits to a website, purchases, historical data, and information included in the product feed). This way the Synerise application can produce relevant recommendations that match preferences of customers and circumstances of displaying the recommendation frame. In Synerise, a user can show recommendations within the following channels: - on the website (through [dynamic content](/docs/campaign/dynamiccontent)) - [emails](/docs/campaign/e-mail) - [web push notification](/docs/campaign/Webpush) - [mobile push notifications](/docs/campaign/Mobile) - mobile applications built based on [Documents](/docs/assets/documents) ## Business applications --- 1. Monetize customers' data and interactions to personalize experience across multiple touchpoints in different communication channels including web, mobile application, email, and many others. 2. Boost conversion at any step of customer journey from home page, category or item page, to cart, to post-purchase activities. 3. Generate top quality real-time recommendations for both recognized, unrecognized, and first-time customers based on various types of interaction. 4. Configure, launch, and deploy models to run and monitor performance of recommendations with only a few clicks with a simple user interface. 5. Tailor recommendation results to your business needs with recommendation configuration settings, including A/B testing, advanced filtering, boosting, and sorting options. 6. Benefit from state-of-the-art machine learning models powered by Synerise proprietary AI engine - Cleora. No need to manually process data ingestion and cleansing processes, models parameters tuning or retraining as framework does it for you. ## Requirements ---
To access AI Recommendations and manage recommendations campaign, you must have the following [permissions](/docs/settings/identity-access-management/permissions#permissions): - Permissions from the **Communications > Recommendations** set (at least **Read** to see the campaigns). - All permissions from the **Assets > Catalogs** set.
- [Prepare an item feed](/docs/ai-hub/recommendations-v2/item-feed-requirements) - Use consistent item identifiers in feed and events; events must include the item identifier - [Configure the AI engine](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-recommendations) - Meet the minimum data requirements of interactions and events. For users of multiple workspaces, we provide the option to train models for a workspace using data from other workspaces in situations where a data shortage in the target workspace prevents model training. This option is available for training the following recommendation models: [Personalized](/docs/ai-hub/recommendations-v2/recommendation-types#personalized), [Section page recommendations](/docs/ai-hub/recommendations-v2/recommendation-types#section-page), [Attribute recommendations](/docs/ai-hub/recommendations-v2/recommendation-types#attribute), [Cross-sell and Cart recommendations](/docs/ai-hub/recommendations-v2/recommendation-types#cross-sell-and-cart-recommendations), [Next interaction](/docs/ai-hub/recommendations-v2/recommendation-types#next-interaction).
The minimum requirements are approximate and allow model training. Meeting the minimum requirements does not ensure optimal operation. The quality of AI models increases with input data volume.
Recommendation type Minimum requirements Recommended requirements
- Personalized
- Section
- Attribute
- Next interaction 		- At least 1,000 unique profiles who visited a product page more than once.
- At least 10,000 of one of the following:		 - 50,000 unique profiles who visited a product page more than once (at least two different items).
- 1,000,000 in total of page.visit events from item pages, product.view events from item views in a mobile application, and transaction.charge events
- The title and category item attributes must be selected as training attributes in the configuration of the AI engine (Synerise > Settings > AI Engine Configuration).
- To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint .
- Similar items1
- Item comparison		 At least one item attribute must be selected in the training attributes section in the AI engine configuration (Synerise > Settings > AI Engine Configuration).
    		The title and category item attributes must be selected as training attributes in the configuration of the AI engine (Synerise > Settings > AI Engine Configuration).
    - To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint
    Visual similarity - Packshot images defined in the item catalog
    - Image resolution of at least 640x480px    		 - Packshot images defined in the item catalog
    - Image resolution of at least 640x480px
    - To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint
    - Cross-sell
    - Cart recommendations    		 At least 1,000 transactions with basket size > 1 - At least 25,000 transactions with basket size > 1
    - To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint
    - Last seen
    - Recent interactions    		 No requirements To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint
    Top items At least 10,000 page.visit events or transaction.charge events with at least 10 unique products To collect recommendation statistics, you must generate a recommendation.click event, which is triggered when a request is sent to the "Item clicked in recommendation" endpoint
    1Similar item recommendations can be created with only the item feed, but events are recommended to build a more effective model. ## Limits Item feed limits: - For [visual similarity](/docs/ai-hub/recommendations-v2/recommendation-types#visual-similarity) recommendation model, the item feed can contain up to 1,000,000 items, regardless of the item feed type. The following are the default limits: - Maximum number of active recommendation campaigns: 1,000 - Maximum number of active and draft recommendation campaigns: 10,000 - Maximum number of AI recommendation models: 25 - Maximum number of items in recommendation: 100 - Maximum length of a filter (IQL string): 10,000 characters The following are the permanent limits (cannot be changed) per a recommendation. The limits apply both for filtering and boosting options: - Maximum number of unique segmentations: 1 - Maximum number of unique aggregates/expressions: 2 - Maximum number of unique customer attributes: 20
    Multiple occurrences of the same analysis (a segmentation, expression, aggregate) or attribute count as one towards the limit.
    ## You may want to read --- - [Recommendation types](/docs/ai-hub/recommendations-v2/recommendation-types) - [How to create recommendation](/docs/ai-hub/recommendations-v2/creating-recommendation-campaign) - [How to use filters in recommendations](/docs/ai-hub/recommendations-v2/recommendation-filters) - [Recommendation statistics](/docs/ai-hub/recommendations-v2/recommendation-statistics)