Interactive Garment Recommendation with User in the Loop
Federico Becattini Xiaolin Chen Andrea Puccia Haokun Wen Xuemeng Song Liqiang Nie Alberto Del Bimbo
Recommending fashion items often leverages rich user profiles and makes
targeted suggestions based on past history and previous purchases. In this
paper, we work under the assumption that no prior knowledge is given about a
user. We propose to build a user profile on the fly by integrating user
reactions as we recommend complementary items to compose an outfit. We present
a reinforcement learning agent capable of suggesting appropriate garments and
ingesting user feedback so to improve its recommendations and maximize user
satisfaction. To train such a model, we resort to a proxy model to be able to
simulate having user feedback in the training loop. We experiment on the
IQON3000 fashion dataset and we find that a reinforcement learning-based agent
becomes capable of improving its recommendations by taking into account
personal preferences. Furthermore, such task demonstrated to be hard for
non-reinforcement models, that cannot exploit exploration during training.