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.

翻译：过去几年间，服装搭配的自动化引起了研究界的广泛关注。现有大多数服装推荐系统侧重于基于视觉和文本特征的成对商品兼容性预测，以此对有多个商品组成的服装组合进行评分，随后根据用户的时尚品味推荐排名靠前的几套服装或包含多套服装的胶囊衣橱。然而，这些系统均未考虑用户对单件服装类型的价格区间偏好，或是对一组商品的整体购物预算。本文提出一个箱装推荐框架——BOXREC，该框架首先收集用户对不同商品类型（即上装、下装和鞋履）的偏好，包括每种类型的价格区间以及特定购物会话的最大购物预算。随后，通过从数据库中检索所有类型的偏好商品（依据用户指定的偏好包括价格区间），生成所有三种偏好商品（分属不同商品类型）的可能组合，并利用一个服装评分框架——BOXREC-OSF对每种组合进行验证。最终，它提供一个装满时尚商品的箱子，使得这些商品的不同组合能够在满足最大购物预算的同时，最大化适用于某场合的服装套数。实验结果表明，BOXREC-OSF在性能上优于基线方法。