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TikTok Launches 'Must Eat' Restaurant Guide Feature in Select US Cities

🔄 Updated 9d ago
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Key points

  • TikTok launched 'Must Eat' for restaurant recommendations.
  • Feature uses over 40 million posts and comments for curation.
  • Users can filter by city, cuisine, price, rating, and location.
  • Available in New York City, Los Angeles, and San Francisco initially.

TikTok Enters Restaurant Discovery Market

TikTok has rolled out a new feature called "Must Eat," designed to help users find restaurants based on community recommendations. This initiative marks TikTok's entry into the restaurant guide sector, directly competing with established platforms such as Yelp and Google Maps.

Leveraging User-Generated Content

The "Must Eat" feature compiles restaurant suggestions from more than 40 million posts and comments on the platform. It allows users to browse restaurants by city and apply filters for cuisine type, price range, user rating, and location, providing a consolidated view of dining options.

Categorization and Booking Options

Recommendations within "Must Eat" are divided into two categories: "Everyday Eats" for casual dining and "Elevated Dining" for special occasions. For certain restaurants, direct booking is available through an integration with OpenTable.

Initial Rollout and Access

The feature is initially available in New York City, Los Angeles, and San Francisco. Users can access "Must Eat" via the search bar, the Local feed, specific restaurant pages, and location links embedded in videos that feature participating establishments.

✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →

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Reporting from

TikTok introduced "Must Eat," a new feature that aggregates community-driven restaurant recommendations from over 40 million posts and comments. This expansion positions TikTok in direct competition with existing restaurant discovery platforms like Yelp and Google Maps, leveraging its video-based recommendation strength.