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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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11#
發(fā)表于 2025-3-23 10:00:56 | 只看該作者
The Importance of Brand Affinity in Luxury Fashion Recommendationsed an engagement uplift of up?to 10%, and applied the embeddings as a content-based recommender to obtain an engagement uplift of up?to 3%. Overall, we are confident of the importance of brand affinity information in recommender systems in the luxury fashion domain.
12#
發(fā)表于 2025-3-23 15:17:10 | 只看該作者
User Aesthetics Identification for Fashion Recommendationsheir various combinations in building such models. Our results show that it is possible to identify a customer’s aesthetic based on this data. Moreover, we found that the use of the textual descriptions of products interacted by the customer led to better classification results.
13#
發(fā)表于 2025-3-23 18:36:42 | 只看該作者
Attention Gets You the Right Size and Fit in Fashionof “translation” problem (from articles to sizes) using an attention-based deep learning model for size and fit prediction. Through extensive experimental results, over millions of customers and articles, we demonstrate how this approach is capable of dealing with multiple customers buying from a si
14#
發(fā)表于 2025-3-24 01:47:54 | 只看該作者
Outfit Generation and Recommendation—An Experimental Studyng online, real-world user data from one of Europe’s largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation. Moreover, we provide insights for models that have not yet been evaluated on this task, specifically, GPT,
15#
發(fā)表于 2025-3-24 02:20:33 | 只看該作者
16#
發(fā)表于 2025-3-24 07:54:54 | 只看該作者
17#
發(fā)表于 2025-3-24 13:17:59 | 只看該作者
https://doi.org/10.1007/978-3-030-66103-8Recommender Systems; Information Retrieval; Information Systems; Machine Learning; AI Artificial Intelli
18#
發(fā)表于 2025-3-24 15:43:53 | 只看該作者
Nima Dokoohaki,Shatha Jaradat,Reza ShirvanyOffers the best contributions of the 2nd workshop on recommender systems in fashion and retail, held in 2020.Presents a state-of-the-art view of recommender systems for e-commerce.Provides readers wit
19#
發(fā)表于 2025-3-24 22:29:16 | 只看該作者
978-3-030-66105-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
20#
發(fā)表于 2025-3-25 02:53:18 | 只看該作者
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