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Titlebook: Recommender Systems in Fashion and Retail; Nima Dokoohaki,Shatha Jaradat,Reza Shirvany Conference proceedings 2021 The Editor(s) (if appli

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樓主: 爆裂
21#
發(fā)表于 2025-3-25 03:38:52 | 只看該作者
22#
發(fā)表于 2025-3-25 09:47:48 | 只看該作者
Probabilistic Color Modelling of Clothing Itemshen clothing items have multiple colors. Moreover, we are able to extract colors along with the probability of them appearing in clothes. The method can provide the color baseline drive for more advanced fashion systems.
23#
發(fā)表于 2025-3-25 13:46:59 | 只看該作者
The Importance of Brand Affinity in Luxury Fashion Recommendationsf this sector. Fashion experts have a strong understanding of the intricacies of the fashion scope. The brands and designers are some of the most important features of this landscape and the affinity between them is not always easy to grasp. This paper proposes an application of state-of-the-art NLP
24#
發(fā)表于 2025-3-25 17:39:27 | 只看該作者
Probabilistic Color Modelling of Clothing Itemsor-based retrieval, fashion design, etc. We aim to develop and test models that can extract the dominant colors of clothing and accessory items. The approach we propose has three stages: (1) Mask-RCNN to segment the clothing items, (2) cluster the colors into a predefined number of groups, and (3) c
25#
發(fā)表于 2025-3-25 21:21:31 | 只看該作者
26#
發(fā)表于 2025-3-26 03:09:34 | 只看該作者
Towards User-in-the-Loop Online Fashion Size Recommendation with Low Cognitive Loads work we study this topic in depth and demonstrate its various complexities focusing in particular on the challenging cold-start problem that arises when no order history is available for a specific customer. We demonstrate the multifaceted value of data obtained by involving the customer in the lo
27#
發(fā)表于 2025-3-26 05:00:59 | 只看該作者
28#
發(fā)表于 2025-3-26 11:38:36 | 只看該作者
The Ensemble-Building Challenge for Fashion Recommendation: Investigation of In-Home Practices and A (e.g. a product the user will purchase), individual garments must function within a wardrobe system, and must ultimately be matched with other garments to build an outfit. The outfit-building challenge is poorly understood in academic literature and professional practice. Here, we present data from
29#
發(fā)表于 2025-3-26 14:12:40 | 只看該作者
30#
發(fā)表于 2025-3-26 20:11:14 | 只看該作者
Understanding Professional Fashion Stylists’ Outfit Recommendation Process: A Qualitative Study to implement ways to maximize clothing use. Artificially intelligent decision support may help users make better purchase decisions as well as daily dressing decisions. However, learning relationships between user and garment features is challenging due to the sparsity of data and the lack of valid
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