[Tech]취향을 저격하는 '추천 알고리즘'의 작동 원리

출처: 방송통신위원회 '취향을저격하는 '추천알고리즘'의 작동원리 유튜브 영상

팜킷의 개인화 추천 알고리즘은 내용기반추천(Contents-based Recommendation)방식으로 식품과 상품의 특성을 사전에 분석하여 적은데이터(Small-data)로 양질의 추천이 가능하다는 특징이 있습니다.

이러한 특징에 대한 이해를 돕기위해 추천 알고리즘의 작동원리  영상을 공유드립니다.

TasteQ helps your customers stay 

longer and engage deeper

TasteQ goes beyond recommendations — it understands your taste

From emerging indie brands to global enterprises, TasteQ delivers truly personalized curation that resonates with every user.


Enhance your competitive edge and accelerate revenue growth

TasteQ delivers AI-driven, preference-based curation that converts insights into measurable sales — all without the need for in-house AI teams or proprietary training data. Fast to deploy, easy to scale, and built for business impact.

TasteQ helps your customers

stay longer and engage deeper

TasteQ goes beyond recommendations — it understands your taste

From emerging indie brands to global enterprises, TasteQ delivers 

truly personalized curation that resonates with every user.


Enhance your competitive edge and accelerate revenue growth

TasteQ delivers AI-driven, preference-based curation that converts insights into measurable sales — all without the need for in-house AI teams or proprietary training data. Fast to deploy, easy to scale, and built for business impact.


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ⓒ 2025 Farmkit

Privacy Policy        Term of use        LinkedIn