Within the context of Salesforce Marketing Cloud's Interaction Studio (formerly Evergage), "recipes" are pre-built configurations for personalized recommendations. These recipes utilize different types of "ingredients" to determine which items to recommend. Let's break down the correct options:
A. Catalog-based and Trending
Verified:This is a type of ingredient used in Interaction Studio recipes.
Explanation:
Catalog-based:This ingredient leverages data from your product or content catalog. It can recommend items based on various catalog attributes like:
Category:Recommending items from the same or related categories as items the user has viewed or interacted with.
Attributes:Recommending items that share specific attributes (e.g., color, brand, size) with items the user has shown interest in.
Keywords:Recommending items whose descriptions or metadata match keywords derived from user behavior.
Trending:This ingredient considers the overall popularity or trending status of items within your catalog, often within a specific timeframe (e.g., "Trending in the last 7 days").
Salesforce Marketing Cloud References:
Interaction Studio Recipes:The Interaction Studio documentation describes the various recipe types and the ingredients they use.
B. Recommendations
Verified:This is a broad category encompassing ingredients that generate recommendations based on various algorithms.
Explanation:
Recommendation Algorithms:Interaction Studio employs different algorithms to generate recommendations, including:
Collaborative Filtering:Recommending items that similar users have liked or interacted with.
Content-Based Filtering:Recommending items that are similar in content or attributes to items the user has shown interest in.
User Affinity:Recommending items based on the user's overall affinity for particular categories, brands, or attributes, calculated from their historical interactions.
Note:"Recommendations" is a more general term. Specific recommendation ingredients might have names like "User-to-Item Affinity," "Item-to-Item Similarity," or use algorithm names directly.
C. Co-Occurrence
Verified:This is a specific type of recommendation ingredient that focuses on items frequently viewed or purchased together.
Explanation:
Co-occurrence Logic:This ingredient identifies items that are often viewed or purchased in the same session or within a short timeframe. It suggests that if a user is interested in item A, they are also likely to be interested in item B because other users have frequently interacted with both items together.
Examples:
"Customers who bought this item also bought..."
"Frequently viewed together"
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