  Over the past few years, automation of outfit composition has gained much
attention from the research community. Most of the existing outfit
recommendation systems focus on pairwise item compatibility prediction (using
visual and text features) to score an outfit combination having several items,
followed by recommendation of top-n outfits or a capsule wardrobe having a
collection of outfits based on user's fashion taste. However, none of these
consider user's preference of price-range for individual clothing types or an
overall shopping budget for a set of items. In this paper, we propose a box
recommendation framework - BOXREC - which at first, collects user preferences
across different item types (namely, top-wear, bottom-wear and foot-wear)
including price-range of each type and a maximum shopping budget for a
particular shopping session. It then generates a set of preferred outfits by
retrieving all types of preferred items from the database (according to user
specified preferences including price-ranges), creates all possible
combinations of three preferred items (belonging to distinct item types) and
verifies each combination using an outfit scoring framework - BOXREC-OSF.
Finally, it provides a box full of fashion items, such that different
combinations of the items maximize the number of outfits suitable for an
occasion while satisfying maximum shopping budget. Empirical results show
superior performance of BOXREC-OSF over the baseline methods.
