Body Measure-aware Fashion Product Recommendations: Evaluating the Predictive Power of Body Scan Data

Fashion product consumer are faced with large and fast changing product o‚erings. Œe fashion purchase decision process is complex, as the consumer has to consider various inƒuencing factors like current fashion trends, what fashion products €t to their personality, and what products €t to their physical appearance like hair colors or body measures. Based on novel technologies, 3D body avatars can be reconstructed from 3D or 2D data. From these avatars, body measures can be determined. Œe objective of this research is to investigate the predictive performance of body measures extracted from a 3D body scanner for predicting fashion item preferences. Œerefore, item preferences and body scans from 200 users were collected. From the body scans, 11 body measures are extracted and integrated into a prediction model using Factorization Machines. Œe results from a cross-validation show, that including body measurements signi€cantly improves the prediction performance of the recommendation model, especially in new user scenarios, when no information about the fashion product preferences of the active user is known.