WP3. Fashion emergent trend detection, user profiling and fashion recommendations

WP3 focuses on the large-scale mining of multimodal fashion data from Mallzee’s consumer base using novel machine learning approaches with the purpose of extracting data-driven fashion trends, consumer segments, and ultimately producing better targeted and richer garment recommendations.

Description of work

T3.1 – Fashion emergent trend detection algorithms (M5-M24)

We will carry out object detection work training models on MLZ’s own set of fashion products imagery, with a goal of high precision in detection quality. After this sub-task is completed, we will apply time-series forecasting methods on MLZ’s own dataset of user preferences, alongside with the visual content detected. This will allow for the forecasting of patterns. Moreover, on the new digital imagery provided by WP2 (after the simulation of new digital garments on 3D avatars), we will be able to predict the success of the garments and test them with real customers to collect their opinions utilizing MLZ’s application.

T3.2 – User profiling and segmentation: (M5-M24)

We will work on the machine learning task of clustering consumers into groups by their fashion preferences. This will subsequently allow the insights generated on new products, such as those provided by Metail, to be segmented at the fashion group level.

T3.3 – Garment recommendations for the consumers: (M5-M24)

We will improve on MLZ’s recommendation algorithm by including the results of task T3.1 (the visual clues extracted from fashion imagery). This will then enable new features in the existing algorithm, which recommend garments to users based on the preferences they expressed on patterns, and subsequently recommend outfit matches which can determine what items go well together.