BOXREC：在线购物中推荐一盒优选穿搭
BOXREC: Recommending a Box of Preferred Outfits in Online Shopping
AI总结：
本文提出BOXREC框架，结合用户对各类服装的价格区间偏好和总预算约束，通过BOXREC-OSF评分生成一盒能最大化适场合穿搭数量的时尚物品组合。
摘要：
在过去几年中，穿搭组合的自动化引起了研究界的广泛关注。现有的大多数穿搭推荐系统侧重于成对物品兼容性预测（使用视觉和文本特征）来对包含多个物品的穿搭组合进行评分，然后根据用户的时尚品味推荐前 n 个穿搭或包含一系列穿搭的胶囊衣橱。然而，这些系统都没有考虑用户对单个服装类型的价格区间偏好，或对一组物品的整体购物预算。在本文中，我们提出了一个盒子推荐框架——BOXREC——它首先收集用户对不同物品类型（即上装、下装和鞋类）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。然后，它通过从数据库中检索所有类型的优选物品（根据用户指定的偏好，包括价格区间），生成一组优选穿搭，创建三个优选物品（属于不同物品类型）的所有可能组合，并使用穿搭评分框架——BOXREC-OSF——验证每个组合。最后，它提供一个装满时尚物品的盒子，使得物品的不同组合在满足最大购物预算的同时，最大化适合某一场合的穿搭数量。实证结果表明，BOXREC-OSF 的性能优于基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
