With the backing of Meta’s machine-learning infrastructure and real-time user personalization, Threads has quickly become a formidable contender to X.com. In this blog post, we cover 4 reasons why they were able to build a state-of-the-art system so quickly:

1. Taking a (For You) Page out of Tiktok
2. Meta’s Ranking Infrastructure Advantage
3. Leveraging Instagram to Solve the Cold-Start Problem
4. Optimizing for Experience Vs. Ads Revenue

1. Taking a (For You) Page out of Tiktok

Many people don’t know this but TikTok started as a recommendation system and machine-learning infrastructure company. ByteDance’s (Tiktok's parent company) original mission was not to create a video-sharing platform; instead, it aimed to offer real-time personalized content recommendations.

Prior to TikTok's inception, ByteDance’s team explored around 100 ideas, ranging from multiple meme apps to a news aggregation platform, and even a video-sharing app. The engine that powered all of them was ByteDance’s recommendation system.

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Ultimately, the video-sharing format emerged as the most promising avenue. As they noticed a growing interest in short-form videos, ByteDance became laser-focused on developing a hyper-personalized video platform, leading to the birth of TikTok, as we know it today.

TikTok's personalized recommendations keeps users engaged for an average of 52 minutes a day. With a real-time recommendation system like Monolith, Tiktok is widely regarded as having a world-class, machine-learning, ranking infrastructure.

2. Meta’s Ranking Infrastructure Advantage

Much like TikTok, Meta has also developed a robust machine-learning ranking infrastructure, allowing them to offer real-time personalized content recommendations. What is clear is that Meta's investment in personalization is paying off, as seen in its latest venture, Threads. Launching with powerful personalization has helped retain users on the platform and enabled content creators to achieve viral success from reliable, positive user feedback.

Without Meta’s machine-learning infrastructure, Thread’s rapid success wouldn’t be possible. Poor personalization would have immediately led to significant retention issues – a death sentence for social media products. Competitors like Minds and Bluesky Social have struggled with personalization on their platforms. This is due to their lack of machine-learning infrastructure and inability to solve the resulting cold-start problem.

3. Leveraging Instagram to Solve the Cold-Start Problem

The cold-start problem refers to the challenge of providing personalized recommendations and tailored experiences to new users when there is insufficient information to make accurate predictions or to understand user preferences. There are two types of cold start problems:

• Cold-start users: Individuals who join a social network with little to no existing connections or activity history. These users lack the followers, or engagement that are often present for established users.

• Cold-start items: Pieces of content, such as posts, articles, or videos that are added to a social network with little to no prior engagement or feedback. These items lack likes, comments, or views, which are crucial for algorithms to personalize recommendations.

There are a number of strategies that social networks use to solve the cold start problem, including:

• Incentivizing users to connect with their friends through rewards and referrals.
• Using algorithmic recommendations to surface relevant content to new users.
• Providing a good onboarding experience by making it easy for new users to understand how the network works.

Threads has a significant advantage of accessing a huge amount of data from day 0 of launch, as it is backed by Instagram’s data. Upon sign-up on Threads, every user has the opportunity to connect with their followers that they had on Instagram.

Threads already knows who you follow, what you’re interested in, the millions of likes you’ve done since you created an account, and even which videos and posts you spend more time watching.

4. Optimizing for Experience Vs. Ads Revenue

Since Meta has deep pockets and less short-term financial pressure, the platform has the luxury of not optimizing for advertising revenue. This gives Threads the perfect opportunity to play social media ‘superhero’ as it doesn’t need ads to sustain the business right now, which ends up being a huge user-experience win. Users see what they want to see. Threads leverages real-time signals such as Impressions, Views, Comments, Likes/Dislikes, Shares, and Follows to keep users engaged and informed constantly. Whether it's the latest news, trends, or interests, Threads ensures that users are engaging in lively discussions that have some relevance to them.

In Conclusion

As Threads continues to grow, it will reshape the digital town square and provide users with a platform to host meaningful text-based conversations. Threads' origin story is similar to TikTok’s, underscoring the importance of real-time recommendations, infrastructure, and personalization in scaling a successful social media product. If you or your company is interested in real-time recommendations, infrastructure, and personalization, you can learn more on the Shaped blog.