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    How the Tiktok algorithm recommends new videos

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    How the TikTok Algorithm Recommends New Videos

    The Tick Tock algorithm is an AI-based recommender system that suggests videos to users based on their preferences. To build this system, a large dataset of users and videos is used. Users interact with videos by liking or watching them, creating positive signals that are used to recommend similar videos to other users. The challenge arises from the vast number of videos available, leading to a massive matrix of user interactions. Machine learning techniques are employed to reduce this matrix into embeddings. Additionally, a shift from collaborative to content-based filtering helps address the issue of new users with no viewing history. By prompting users to indicate their preferences upon signing up, Tick Tock can categorize them and provide relevant video suggestions, circumventing the "cold start" problem.


    Recommender system, Tick Tock algorithm, AI, user preferences, video recommendations, machine learning, collaborative filtering, content-based filtering, user categorization, cold start problem.


    1. What is the primary function of the Tick Tock algorithm?
      • The Tick Tock algorithm serves as a recommender system that suggests videos to users based on their preferences and viewing history.
    2. How does Tick Tock address the issue of a vast number of videos available for recommendation?
      • Tick Tock uses machine learning techniques to reduce the complexity of the user-video interaction matrix and employs content-based filtering to offer personalized recommendations to users, especially those with no viewing history.
    3. How does Tick Tock overcome the "cold start" problem for new users?
      • Tick Tock prompts new users to specify their preferences upon signing up, enabling the system to categorize them and provide relevant video suggestions from the outset.

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