| 書目名稱 | Fashion Recommender Systems |
| 編輯 | Nima Dokoohaki |
| 視頻video | http://file.papertrans.cn/342/341244/341244.mp4 |
| 概述 | The first book to cover state of the art academic and industrial research on recommendation systems in fashion and retail e-commerce.Brings together a group of top scholars on most recent machine lear |
| 叢書名稱 | Lecture Notes in Social Networks |
| 圖書封面 |  |
| 描述 | .This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field..Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations.?.The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be avery useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfit |
| 出版日期 | Conference proceedings 2020 |
| 關(guān)鍵詞 | recommender systems; information systems; information retrieval; machine learning; text mining; fashion p |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-55218-3 |
| isbn_softcover | 978-3-030-55220-6 |
| isbn_ebook | 978-3-030-55218-3Series ISSN 2190-5428 Series E-ISSN 2190-5436 |
| issn_series | 2190-5428 |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機(jī)版|小黑屋|
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