What Hair-Care Apps Can Learn From Personalization and Recommendation Systems

Voice of Women leaders — Tue, 25 Aug 2026 07:40:14 +0000

Technology is becoming increasingly personalized. Streaming platforms recommend movies, shopping websites suggest products, and fitness applications adapt plans according to individual users.

The same approach can be useful in personal beauty and hair-care technology.

Curly hair is a good example because users can have significantly different needs even when they describe their hair using the same general term.

Why One Recommendation Does Not Work for Everyone

A recommendation system that simply categorizes every user as having “curly hair” would have limited usefulness.

A better system could consider multiple characteristics:

Curl pattern
Hair density
Hair thickness
Moisture needs
Climate
Styling preferences
Previous product responses

This creates a much more personalized user profile.

From Classification to Personalization

A basic application could begin by asking users a series of questions about their hair.

For example, the system might identify whether the user's hair is wavy, curly, or coily. Users could then provide additional information about dryness, frizz, volume, and styling goals.

Understanding different curl types and their characteristics can provide a useful foundation for designing such a classification system.

The technology does not need to stop at classification.

A recommendation engine could analyze user feedback over time and adjust future suggestions.

Machine Learning Could Improve Recommendations

Imagine a system where users record how their hair responds to different routines.

The application could collect structured feedback such as:

Product used
Amount applied
Weather conditions
Drying method
Result after 24 hours
Frizz level
Curl definition

Over time, machine-learning models could potentially identify patterns between these variables.

For example, users in high-humidity environments may receive different routine suggestions than users living in dry climates.

Privacy Should Be Part of the Design

Personalization also creates privacy considerations.

A hair-care application should collect only information that is necessary for its functionality. Users should understand what data is being collected, why it is needed, and how it is stored.

Personalization should improve the user experience without becoming invasive.

The Bigger Technology Opportunity

Beauty technology is moving beyond simple product catalogs.

The next generation of applications could combine computer vision, recommendation engines, user feedback, and environmental data to create highly personalized experiences.

Curly hair is just one example.

The broader lesson for developers is simple: good personalization begins with understanding that users who appear similar at a high level can have very different needs underneath.

DEV Community: Voice of Women leaders

What Hair-Care Apps Can Learn From Personalization and Recommendation Systems