  Personalized fashion recommendation aims to explore patterns from historical
interactions between users and fashion items and thereby predict the future
ones. It is challenging due to the sparsity of the interaction data and the
diversity of user preference in fashion. To tackle the challenge, this paper
investigates multiple factor fields in fashion domain, such as colour, style,
brand, and tries to specify the implicit user-item interaction into field
level. Specifically, an attentional factor field interaction graph (AFFIG)
approach is proposed which models both the user-factor interactions and
cross-field factors interactions for predicting the recommendation probability
at specific field. In addition, an attention mechanism is equipped to aggregate
the cross-field factor interactions for each field. Extensive experiments have
been conducted on three E-Commerce fashion datasets and the results demonstrate
the effectiveness of the proposed method for fashion recommendation. The
influence of various factor fields on recommendation in fashion domain is also
discussed through experiments.
