How Personalization and ML Could Transform Hair-Care App Recommendations

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A new analysis from DEV Community explores how recommendation system techniques used by streaming and shopping platforms could be applied to hair-care applications. The piece argues that broad categories like 'curly hair' are insufficient, and that effective apps should capture multiple attributes such as curl pattern, dryness, frizz, and styling goals to build detailed user profiles. Machine learning models could then analyze user feedback over time to refine suggestions, accounting for factors like local climate and humidity. The article also highlights privacy as a key design consideration, urging developers to collect only necessary data and keep users informed about its use. Developers are encouraged to treat seemingly similar users as individuals with distinct needs, a principle the piece positions as central to the next generation of beauty technology.

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ProgrammingDEV Community ·

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