Facebook Marketplace AI system

UPDATED MAR 12, 2025
The content you see on your Facebook Marketplace is selected, ranked and delivered to you by an artificial intelligence (AI) system. Within one AI system, multiple machine learning models work together to deliver your experience. These models and their input signals are dynamic and they change frequently as the system learns and improves over time.

Overview of Facebook Marketplace

When you view and interact with Facebook, including Facebook Marketplace feed, one of the underlying AI systems recommends relevant Marketplace listings. For example, you can see items for sale in categories such as home goods, pet supplies and sporting goods. Your feed might also include other recommendations, such as sellers and content you may be interested in.
Note: Your Marketplace feed might also include boosted listings and advertisements. That content is not powered by the AI we describe in this system card.
How Facebook Marketplace works
The AI system behind Facebook Marketplace feed automatically determines which posts show up in your feed by predicting the listings, sellers and content you’re most likely to be interested in. These predictions are based on a variety of factors, including how you’ve interacted with previous listings and with Facebook content. Here’s how it works:
  1. Gather inventory
    First, the system gathers listings to show you from a variety of sources using different methods. For example, products may be included in your feed based on how recently the product was listed for sale. Products may also be included in your feed based on relevance, by comparing the product characteristics to your activity history.
    The AI system generally hides product categories you’ve chosen to block and then produces a subset of listings for further processing. This does not include listings that were reviewed and subsequently rejected based on Meta’s Commerce Policies. Then, the system sends around 1,000 listings on to the next stage.
  2. Leverage signals
    Now, the system considers a variety of input signals based on your activity on Facebook, including Facebook Marketplace. These signals may also include location, category, condition or price of the product or seller.
  3. Make predictions
    From there, the AI system has models that help it make predictions about listings, sellers and content you’ll find most relevant. The system applies certain integrity processes to help reduce the distribution of content that is fraudulent, low-quality or that goes against our Commerce Policies. The system also hides content that—while not against Commerce Policies—you may not want to see, based on other listings you’ve hidden.
  4. Rank listings by score
    Finally, the system calculates a score for each listing, seller or content and puts them in order by this score. Listings that the system predicts will provide more value for you are shown to you first.
How to customize what you see
Your experience on Facebook Marketplace is personalized based on your activity, and you have options to control or customize what you see. Below, we describe how to do this with different in-product features. Options shown here may not be available to everyone.
Browse in a non-personalized way
You can browse Marketplace in a way that’s not personalized specifically to you. Learn more.
Report listing
You can report a listing, which triggers a review that will remove the listing if it goes against our Commerce Policies.
Choose categories
Marketplace lets you choose categories and subcategories to browse and search different listings. Some categories have category-specific filters, such as “vehicle type” in the “vehicle” category.
Set location
You can set the location or radius of where you want to search for local listings in your country. This location can be different from the one on your Facebook profile.
Hide listing
You can hide listings so they no longer show up when browsing or searching Marketplace. This also decreases the likelihood of seeing similar listings in the future.
Identify interests
Marketplace may prompt you to identify your preferences and interests in areas such as specific categories or brands. While optional, these choices customize your experience so that you'll see more of what you’re interested in.
How the AI delivers content to you
We want you to see content you enjoy and find interesting. To achieve this, the AI system has models that help it make predictions about content you'll find most relevant and valuable. These prediction models use underlying input signals to help select content you're most likely to engage with.
Below are some of the significant predictions–and input signals that inform them–that we use in this AI system.
Whether or not you will cross out the listing
Signals influencing this prediction include:
  • How many times you have clicked to hide specific product listings, categories, or channels
  • Product category, a high-level classification of the product's type or purpose.
  • How many times you have clicked to hide specific product listings, categories, or channels
Whether or not you will click into seller profile of the listing
Signals influencing this prediction include:
  • How many times you’ve saved product listings in a specific channel
  • How many times you’ve saved product listings
  • How long since buyer last save
  • How user accessed service (iOS, Android, iPad, Tablet, Instagram...)
Whether or not you will attempt to share the listing
Signals influencing this prediction include:
  • The keywords of product listings you’ve saved or shared
  • How long you’ve spent looking at product listings
  • How many times you’ve attempted to share product listings
  • The keywords of product listings you’ve saved or shared
  • How long you’ve spent looking at product listings
How likely you are to start a conversation with a seller about a listing
Signals influencing this prediction include:
  • Number of specific commerce tag IDs User clicked in the last 7 day
  • How many times you’ve LONG_CLICK (event) product listings in a specific FPT leaf category in 60 days
  • Number of Keyword matched between user recent engagement and the product
  • How many times you’ve IMPR_PDP_PHOTO_CAROUSEL (event) product listings in a specific FPT leaf category in 60 days
  • Number of Image Hash ID User clicked in the last 7 day
How likely you are to swipe and click a photo when viewing the product details page of the listing
Signals influencing this prediction include:
  • How user accessed service (iOS, Android, iPad, Tablet, Instagram...)
  • How many times buyer clicked
  • How many times buyer clicked photo on PDP on specific channel
  • How many times buyer clicked photo on PDP
  • Count of photo open on this product / Count of click on this product
How likely you are to communicate with a seller multiple times
Signals influencing this prediction include:
  • How many times seller messaged
  • How many time buyer sends a message
Whether or not you will click on the listing
Signals influencing this prediction include:
  • Count of product detail page scroll on this product
  • Product click through rate upper confidence bound
  • User engaged keyword id in last 60 days, ranked based on engagement score
  • Number of Keyword matched between user recent engagement and the product
  • Price of the product
Whether or not you will save the listing
Signals influencing this prediction include:
  • Count of product detail page scroll on this product
  • Distance of the product to the buyer
  • How long you’ve spent looking at product listings
  • Price of the product
  • Buyer's country location