Understanding User Preferences Through TikTok: How to Use the API to Retrieve Favorite Videos and Analyze User Behavior

In today’s data-driven world, social media platforms have become key gateways for understanding user behavior and preferences. As one of the most influential short-form video platforms globally, TikTok holds immense potential for data mining. Among various user interactions, the act of "liking" a video is one of the most genuine indicators of interest. Through the LuckData TikTok API, developers and analysts can effortlessly retrieve a user's list of favorite videos and incorporate it into larger data strategies alongside other endpoints such as user posts, followers, and interactions.

This article will dive deep into the get user favorite videos endpoint of LuckData's TikTok API. We’ll explore how to implement it technically, understand the data structure, discuss practical applications, and highlight how this data can be cross-referenced with other sources to build a comprehensive view of user behavior.

Why Analyze a User’s Favorite Videos?

Unlike publishing or commenting, liking a video on TikTok is a low-friction, spontaneous action that often reveals a user’s true preferences. Here’s why it matters:

  1. Personalized Recommendations: Likes reveal content preferences and help build more accurate recommendation engines.

  2. Audience Insights for Brands: Analyzing the type of videos a target audience favors enables more effective content and ad strategies.

  3. Behavioral Profiling: When combined with publishing and engagement data, favorite videos round out a user’s digital profile.

  4. Trend Forecasting: Understanding what videos are frequently favorited can help detect early signs of viral content or trending themes.

What is LuckData and How Does the API Work?

LuckData offers an intuitive and powerful TikTok API, designed to fetch structured data from the platform without requiring users to build their own web scraping systems. Its endpoints support features like:

  • User info

  • User post videos

  • Follower/following lists

  • Video info

  • Comment lists

  • Trending challenges and regions

  • Ads, music, collections, and more

The get user favorite videos endpoint lets you access the list of videos that a particular user has liked. You can query using either the user_id or unique_id, and you’ll receive a JSON response with detailed metadata on each video.

API Parameters Overview

  • user_id: The numerical TikTok ID of the target user.

  • unique_id: The TikTok username (e.g., @tiktok) as an alternative to user_id.

  • count: Number of videos to return per request.

  • cursor: Used for pagination; initially set to 0.

  • X-Luckdata-Api-Key: Your personal API key for authentication.

Sample Python Code to Fetch Favorite Videos

Here’s a basic example using Python to retrieve favorite videos from a TikTok user:

import requests

headers = {

'X-Luckdata-Api-Key': 'your_luckdata_key'

}

response = requests.get(

'https://luckdata.io/api/tiktok-api/ZFr5azigd1vx?count=10&cursor=0&user_id=6741307595983946754',

headers=headers,

)

favorite_videos = response.json()

print(favorite_videos)

This request fetches 10 liked videos from the specified user ID. You can update the cursor to retrieve additional data pages.

Understanding the Response Structure

The API returns a JSON object containing video-level metadata. Key fields include:

  • video_id: Unique video identifier

  • desc: Video description and hashtags

  • create_time: Timestamp when the video was posted

  • play_count: Number of views

  • digg_count: Number of likes

  • comment_count: Number of comments

  • share_count: Number of shares

  • duration: Video length

  • cover: Thumbnail image URL

  • music_title: Music used in the video

  • is_ad: Indicates if the video is sponsored content

This structure is designed to be both human-readable and machine-processable, making it ideal for analytics pipelines.

Who Can Benefit from This Data?

  1. Social Media Managers: Understand what content your followers are interested in and tailor your strategy accordingly.

  2. Digital Marketers: Identify commonalities in user interests to better target ads and partnerships.

  3. Data Scientists: Feed this data into user clustering or trend analysis models.

  4. MCNs & Influencer Agencies: Profile potential influencers by comparing their fans' preferences with their own content.

Plans and Access Tiers

LuckData offers a flexible pricing structure:

  • Free: 50 requests/month, 1 request per second

  • Basic ($47/month): 26,000 credits/month, 5 requests/second

  • Pro ($237/month): 158,000 credits/month, 10 requests/second

  • Ultra ($660/month): 550,000 credits/month, 15 requests/second

All plans offer access to the full range of features—no feature limitations, even for free users.

Advanced Use Cases and Data Strategy

Fetching data is only the first step. Here are some ways to put it to practical use:

  1. Build User Interest Tags: Use NLP on descriptions and hashtags to classify users by interest.

  2. Power Recommendation Engines: Combine favorites and posts to fuel machine learning recommendation systems.

  3. Audience Segmentation: Use favorite video attributes to cluster users by behavior or content affinity.

  4. Trend Discovery: Aggregate liked videos across thousands of users to find rising topics or viral patterns.

  5. Combine with Other Endpoints: Create richer profiles by integrating data from posts, followers, and comments.

Development Best Practices

  • Pagination: Use the cursor parameter to paginate through large datasets.

  • Caching: Avoid repeat requests by caching known user data.

  • Automation: Use task schedulers like cron or workflow tools like Airflow to automate data pulls.

  • Error Handling: Implement retries and graceful error fallback to deal with API rate limits or downtime.

  • Visualization: Import data into BI tools like Power BI or Tableau to produce dashboards and reports.

Conclusion

User "likes" on TikTok represent a powerful behavioral signal. With LuckData’s API, accessing this data is simple, cost-effective, and highly scalable. Whether you're building a data product, running a digital marketing campaign, or just trying to better understand your audience, the ability to retrieve and analyze favorite videos can provide you with a competitive edge.

Now is the time to unlock this capability, plug it into your tech stack, and let the data speak for itself. Start small—fetch one user’s favorites—and soon, you’ll be uncovering patterns that tell compelling stories hidden in the TikTok scroll.

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