Facebook is one of the most popular social media platforms in the world, with over 2.8 billion monthly active users as of Q4 2020. As a platform built around connecting friends, family, and communities, search plays a key role in helping users find people and content on Facebook. But what is the name of Facebook’s search engine?
In this article, we’ll take a look at the history and evolution of Facebook search, examine how it works under the hood, and reveal the name of the proprietary search technology that powers searching on Facebook.
The Evolution of Search on Facebook
When Facebook first launched in 2004, search was not a built-in functionality. At the time, Facebook was limited to students at Harvard and focused more on browsing your social network rather than searching for content.
It wasn’t until 2007 that Facebook introduced its first search feature, initially allowing users to find people by name. Over the next few years, Facebook continued expanding and improving search capabilities:
| Year | Search Milestone |
|---|---|
| 2007 | Name search introduced |
| 2008 | Ability to search by school or workplace added |
| 2009 | Search results improved to match full names, not just first names |
| 2010 | Graph Search launched, allowing structured searches like “Friends of friends who live in New York” |
Graph Search represented a major evolution in Facebook’s capabilities, moving beyond keyword searches to contextual, natural language queries. However, it remained limited to searching for people and connections rather than posts or pages.
The ability to search for content across public posts came later. In 2013, Facebook introduced keyword search for public posts, followed by hashtag search in 2014. This enabled users to discover posts even if they weren’t shared directly within their own social graph.
The Unified Search Experience
In 2016, Facebook took steps to unify and improve search across its various surfaces. It combined the previously separated search functions (people, pages, groups, posts) into one search bar at the top of the app and website.
Behind the scenes, this new search experience was powered by Facebook’s own homegrown search engine, which utilized advanced natural language processing, machine learning, and graph analysis to understand searches and return relevant results.
How Does Facebook Search Work?
So how does this proprietary Facebook search technology function under the hood? Let’s take a look at some of the key components:
Understanding Search Intent
One of the hardest challenges in search is understanding what the user is looking for based on a short text query. Facebook uses natural language processing and machine learning algorithms to analyze search queries and deduce intent.
For example, a search for “Starbucks near me” indicates the user is looking for nearby locations of that business. A search for “gift ideas for mom” implies the user wants shopping recommendations suitable as mother’s day gifts.
By determining query intent, Facebook can return results optimized for the presumed goal of that search.
Leveraging the Social Graph
Facebook’s knowledge of social connections provides useful signals for understanding search context and improving relevance. If you search for your friend’s name, Facebook knows to highlight your actual friend rather than strangers who happen to have the same name.
For searches with local intent, like “pizza places nearby,” Facebook can prioritize results frequented by friends and others in your extended social network. And for broader informational queries, it can identify trusted sources you or your connections follow.
Ranking Signals Specific to Social Media
Facebook search incorporates specialized ranking factors optimized for its context as a social media platform, unlike traditional web search engines. These include social signals like:
– How many likes, comments, and shares a post has
– How many friends or followers an entity has
– How engaging or trustworthy a page’s posts tend to be
By factoring in these social signals, Facebook aims to surface the most reputable and interesting results on its platform.
Continuous Evolution with AI
To keep improving search relevance, Facebook employs artificial intelligence techniques like deep learning. By training neural networks on search query logs and result click data, Facebook can iterate and enhance its systems to better understand searcher intent and needs.
As new trends emerge and patterns shift on the network, Facebook’s AI models adapt in real-time to changing search behavior. This continuous optimization helps explain why Facebook search has gotten significantly smarter and more intuitive over the years.
Unified Search Architecture
Pulling all of these components together required building a robust unified search architecture capable of fetching and ranking results from across Facebook’s social graph. At a high level, the company’s search engine works like this:
| Stage | Description |
|---|---|
| Indexing | Content, profiles, entities are indexed from Facebook, Instagram, Messenger, etc. |
| Query Analysis | Natural language processing determines intent |
| Retrieval | Candidate results fetched based on query understanding |
| Ranking | Results scored and ranked using hundreds of relevance signals |
| Serving | Final ranked results delivered to user’s search interface |
This simplified architecture abstracts away considerable complexity in terms of scale. Facebook’s indexing and search systems have to process billions of pieces of content across multiple apps and surfaces. The search infrastructure handles over 3.5 billion queries per day as of 2021.
To achieve this requires enormous investments in data centers, caching systems, and data optimization. Facebook’s search architecture runs on thousands of servers located across the globe.
The Official Name: Graph Search
Now that we’ve explored Facebook’s search capabilities in depth, what is this proprietary search technology actually called? The official name is **Graph Search**.
Yes, Graph Search was initially a separate structured search feature launched in 2013. But since the unified search rollout, Graph Search has evolved to encompass all search functionality on Facebook.
So in summary:
– **Graph Search** refers to the underlying search engine and infrastructure powering experiences like search bars, search results, search suggestions, and search filters across Facebook.
– It utilizes natural language processing, machine learning, and Facebook’s social graph data to understand queries, retrieve relevant results, and optimize ranking.
– While first launched as its own product, Graph Search now represents Facebook’s unified search architecture and systems across its family of apps.
The Future of Search on Facebook
Facebook is constantly innovating to improve Graph Search and search experiences. Some emerging areas of focus include:
Conversational Search
Moving beyond keyword searches to more natural conversational interfaces. Facebook is exploring voice search and allowing searches within the context of continuous messaging conversations.
Visual Search
Enabling searches based on images, not just text. Users could snap a picture of an item and find where to buy it or search for visually similar content.
Search with AI
Leveraging AI to directly answer questions rather than just returning result links. Facebook is expanding its knowledge graph to power semantic search abilities.
AR/VR Search
As augmented and virtual reality evolve, Facebook aims to pioneer search innovations tailored for immersive experiences.
With its immense scale and resources, Facebook has the incentive and means to push the boundaries of what’s possible with search. The company that pioneered social search aims to remain at the forefront as search keeps evolving.
Conclusion
Facebook has come a long way from its early days without any search capabilities. Driven by user needs to discover people and content, Facebook developed its Graph Search engine to power robust search experiences. By leveraging AI, social signals, and a unified architecture, Graph Search aims to provide the most relevant results tailored specifically to Facebook’s context as a social network. As search continues advancing, Facebook will remain focused on innovation to help connect people with the most useful information across its family of apps.