How to Build a Skincare Recommender System Using Real Product Data

With the rise of personalized beauty, recommender systems have become essential for skincare retailers, brands, and apps. But building one isn’t just about fancy algorithms — it starts with clean, structured data.

In this guide, we’ll walk through how to build a skincare recommender system using real product data from BeautyFeeds.io.

Step 1: Get the Right Dataset

Before you write a single line of code, you need solid data. BeautyFeeds provides structured .xlsx datasets with fields like:

• Product name & brand
• Retailer source (e.g., Dermstore, Mecca)
• Category (serum, moisturizer, cleanser, etc.)
• Ingredients list (INCI format)
• Price, SKU, image URL
• Optional: customer reviews, ratings

Explore sample data here

Step 2: Clean & Preprocess

Typical cleaning steps include:

• Normalize ingredients: Convert INCI names to lowercase, split by commas, remove duplicates.
• Standardize categories: Group similar products (e.g., “night cream” = “moisturizer”).
• Filter active products: Drop discontinued or placeholder entries.

Optional NLP: Use TF-IDF or Word2Vec on ingredients to represent products in vector space.

Step 3: Choose a Recommendation Strategy

There are 3 main types of recommenders. We’ll focus on content-based filtering, which is ideal when user data is limited.

Content-Based Example: Ingredient Similarity

1. Vectorize ingredient lists (e.g., TF-IDF, BERT embeddings)
2. Calculate cosine similarity between products
3. Recommend products with the most similar ingredient profiles

Bonus: Use product categories and skin type tags (if available) to refine relevance.

Step 4: (Optional) Add Personalization

If you have user data (e.g., skin type, past purchases):

• Create user profiles based on preferred ingredients or product types
• Blend content-based + collaborative filtering (hybrid model)

You can also onboard users with a quiz to help cold-start recommendations.

Step 5: Evaluate and Iterate

Use metrics like:

• Precision @ K: How many recommended items were relevant?
• Diversity: Are you recommending different brands/categories?
• User feedback: Are recommendations improving engagement or conversions?

Tip: A/B test changes when possible to measure impact.

What You Can Build

With real product and ingredient data, you can create:

• Interactive skincare quizzes or product finders
• Ingredient-based filters (e.g., fragrance-free, paraben-free)
• AI-powered product advisors for ecommerce or mobile apps

Ready to Build?

Get high-quality, structured skincare product data from BeautyFeeds.io. Whether you’re prototyping a personal project or launching a production-grade AI tool, the data is clean, rich, and ready to power your beauty-tech ideas.

→ Browse available datasets now