# Recommendations from Vertex AI Search

Google Cloud Recommendations AI, or Recommendations from Vertex AI Search, is a managed service that helps ecommerce websites personalize product recommendations for customers. It uses AI and machine learning to analyze user behavior, then recommends items that are most likely to be of interest to them.

## Types of Models

Google Cloud's Retail API includes several pre-built AI models. Some models will require ingesting user event data for **60 to 90 days** before they can be trained. Each model should be placed on its intended page type for best results.

!!!success Serving Configurations
Recommendations AI models can be attached to **one** [serving configuration](/google-cloud-vertex-ai-search-for-commerce/serving-configs.md).
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### Recently Viewed Items

The built-in, default model that shows products that users recently viewed. This model reminds customers of what they were interested in, saving them time and effort from having to search for those items again. This is especially helpful if they were interrupted during their browsing session or if they were considering multiple options.

[!badge variant="info" text="Product Pages"] [!badge variant="success" text="Ready Immediately!"]

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### Recommended for you

Predicts products that users will most likely engage with or purchase based on their shopping or viewing history. Recommended products act as a helpful nudge, highlighting options that fit the customer's needs and preferences, simplifying the decision-making process. Providing recommendations that feel like they were handpicked fosters a sense of connection and value and shows the customer that your store understands their taste and is invested in providing a tailored experience.

[!badge variant="info" text="Home Page"]

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### Others you may like

Predicts products that users will most likely engage with or purchase based on their shopping or viewing history and its relevance to specific products. Sometimes customers get stuck in their own search bubbles, repeatedly encountering the same products. These recommendations break this cycle, introducing them to hidden gems, complementary items, or unexpected delights they might not have stumbled upon otherwise.

[!badge variant="info" text="Product Pages"]

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### Frequently bought together

Predicts product that are frequently bought together based on the product pages visited during the same shopping session. By showcasing items commonly purchased alongside the product the customer is viewing, it eliminates the need to search for these accessories or complementary items separately. This is especially helpful if they are unfamiliar with the product or its ecosystem.

[!badge variant="info" text="Product Pages"] [!badge variant="info" text="Cart Pages"]

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### Similar items

Predicts products that have the mostly similar attributes to the products on the page. The customer may love the general idea of the product they're viewing, but perhaps something about it, like the color, size, or material, isn't quite right. Similar items show alternative options that address their specific preferences, leading to a potential purchase.

[!badge variant="info" text="Product Pages"] [!badge variant="success" text="Quick to Train!"]

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### Buy it again

Predicts products for a user to purchase again based on their purchase history. Showing a customer their past purchases readily available creates a sense of recognition and personalization. It shows the customer that the store remembers their preferences and caters to their individual needs.

[!badge variant="info" text="Any Page"]

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### On sale

Recommends products that are on sale based on their _current_ and _original_ prices. The most common reason customers use this model is to find products at a discounted price. Saving money is always a motivator, and "On Sale" sections highlight deals that can lead to significant savings compared to regular prices.

[!badge variant="info" text="Any Page"]

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!!!info Advanced Model
The **Page-level Optimization** AI model can be applied to many page types and automatically optimizes the entire page with multiple recommendation panels.
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!!!success Model Training Frequency
Training a model unnecessarily can create additional Google Cloud costs. We recommend using the **default settings** for each model and pausing model training if your storefront traffic becomes stagnant.
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## Business Objectives

Some AI model types allow you to select different business objectives and will have different training data requirements. The business objectives are typically one of:

### Click-Through Rate

The model will recommend products that optimize for the **highest number of clicks** on the recommended products.

### Conversion Rate

The model will recommend products that are **most likely to complete a purchase** on your website.

### Revenue per Session

The model will recommend products that are **most likely to increase the amount of revenue generated** from each purchase.