#recommendation-systems

#recommendation-systems

The Washington Post analyzed TikTok usage, finding what topics the algorithm nudges users towards more: TikTok's algorithm favors mental health content over many other topics, including politics, cats and Taylor Swift, according to a Washington Post analysis of nearly 900 U.S. TikTok users who shared their viewing histories. The analysis found that mental health content is stickier than many other videos: It's easier to spawn more of it after watching with a video, and harder to get it out of your feed afterward.

Karrot, a leading platform for building local communities in Korea, uses a recommendation system to provide users with personalized content on the home screen. The system consists of the recommendation machine learning model and a feature platform that acts as a data store for users' behaviour history and article information. As the company has been evolving the recommendation system over recent years, it became apparent that adding new functionality was becoming challenging, and the system began to suffer from limited scalability and poor data quality

Improvements in our recommendation systems will also become even more leveraged as the volume of AI-created content grows. Social media has gone through two eras so far. First was when all content was from friends, family, and accounts that you followed directly. The second was when we added all the creator content. Now, as AI makes it easier to create and remix content, we're going to add yet another huge corpus of content on top of those. Recommendation systems that understand all of this content more deeply and can show you the right content to help you achieve your goals are going to be increasingly valuable.