Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. 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 a user’s preference of price range for individual clothing types or an overall shopping budget for a set of items. In this article, 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. We create an extensively annotated dataset of male fashion items across various types and categories (each having associated price) and a manually annotated positive and negative formal as well as casual outfit dataset. We consider a set of recently published pairwise compatibility prediction methods as competitors of BOXREC-OSF. Empirical results show superior performance of BOXREC-OSF over the baseline methods. We found encouraging results by performing both quantitative and qualitative analysis of the recommendations produced by BOXREC. Finally, based on user feedback corresponding to the recommendations given by BOXREC, we show that disliked or unpopular items can be a part of attractive outfits.

中文翻译：

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BOXREC

时尚服装通常由专业的时尚达人创造，他们利用自己的创造力和对时尚的深入理解来制作有吸引力的服装。在过去的几年里，服装组合的自动化受到了研究界的广泛关注。大多数现有的服装推荐系统都专注于成对的项目兼容性预测（使用视觉和文本特征）来对具有多个项目的服装组合进行评分，然后根据用户的时尚推荐前 n 个服装或具有一组服装的胶囊衣橱品尝。然而，这些都没有考虑用户对单个服装类型的价格范围或一组物品的总体购物预算的偏好。在本文中，我们提出了一个盒子推荐框架——BOXREC——它首先收集不同项目类型（即上装、下装和鞋类）的用户偏好，包括每种类型的价格范围和特定购物的最大购物预算会议。然后，它通过从数据库中检索所有类型的首选项目（根据用户指定的偏好，包括价格范围）来生成一组首选服装，创建三个首选项目的所有可能组合（属于不同的项目类型），并使用验证每个组合装备评分框架——BOXREC-OSF。最后，它提供了一个装满时尚单品的盒子，这样不同的单品组合可以最大限度地增加适合某个场合的服装数量，同时满足最大的购物预算。我们创建了一个包含各种类型和类别（每个都有相关价格）的男性时尚物品的广泛注释数据集，以及一个手动注释的正负正装以及休闲装数据集。我们将一组最近发布的成对兼容性预测方法视为 BOXREC-OSF 的竞争对手。实证结果表明 BOXREC-OSF 优于基线方法。通过对 BOXREC 提出的建议进行定量和定性分析，我们发现了令人鼓舞的结果。最后，根据与 BOXREC 给出的推荐相对应的用户反馈，我们表明不喜欢或不受欢迎的物品可以成为有吸引力的服装的一部分。