Fashion Recommendation Based on Style and Social Events

TL;DR

This paper enhances fashion recommendation systems by integrating style and social event context through emotion-based color analysis and event classification, improving the relevance of outfit suggestions.

Contribution

It introduces a novel approach combining style and event classifiers into existing recommendation frameworks for more context-aware fashion suggestions.

Findings

Improved recommendation relevance for social events.

Effective use of color emotion analysis for style detection.

Integration of classifiers enhances user satisfaction.

Abstract

Fashion recommendation is often declined as the task of finding complementary items given a query garment or retrieving outfits that are suitable for a given user. In this work we address the problem by adding an additional semantic layer based on the style of the proposed dressing. We model style according to two important aspects: the mood and the emotion concealed behind color combination patterns and the appropriateness of the retrieved garments for a given type of social event. To address the former we rely on Shigenobu Kobayashi's color image scale, which associated emotional patterns and moods to color triples. The latter instead is analyzed by extracting garments from images of social events. Overall, we integrate in a state of the art garment recommendation framework a style classifier and an event classifier in order to condition recommendation on a given query.