Instagram User Embedding and Fashion Photo Recommendation Using "likes" of Fashion Photos

Jaeyoung Lee, Younghoon Kim

http://doi.org/10.5626/JOK.2021.48.11.1235

As individual preference of fashion styles diversifies, demands for research recommending personalized fashion are increasing. Recently, with the development of deep learning technology, many studies have been conducted using deep learning to extract features from fashion photos and use them for recommendations. In this work, we exploit social network data to consider users and fashion styles in recommending fashion photos. Since social network users tend to post fashion photos in their preferred style and tag them with “Like“, social network data are very important for understanding relationship between users and fashion photos. We propose a technique to map users and fashion photos into the same vector space using social network data structure which consists of users and fashion photos. Especially, it is possible to use our method to recommend fashion photos that a user might prefer by mapping users and fashion photos not used for learning into a vector space without additional learning.