图 书 馆
街头时尚图片的大规模视觉推荐Large Scale Visual Recommendations from Street Fashion Images |
|
| 课程网址: | http://videolectures.net/kdd2014_jagadeesh_visual_recommendations... |
| 主讲教师: | Vignesh Jagadeesh |
| 开课单位: | 易趣研究实验室 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  我们描述了一个完全自动化的大规模时尚视觉推荐系统。我们的重点是有效地利用大量在线时尚图像及其丰富的元数据的可用性。具体来说，我们在确定性时尚推荐器（DFR）和随机时尚推荐器（SFR）中提出了两类数据驱动模型来解决这个问题。我们通过对大规模数据集的广泛实验来分析这些算法的相对优点和缺陷，并根据颜色科学的现有思想将它们作为基准。我们还说明了通过这些实验学到的关键时尚洞察力，并展示了如何利用它们来设计更好的推荐系统。提议模型的工业适用性是在移动时尚购物的背景下。最后，我们还概述了一个大规模带注释的时尚图像数据集（Fashion 136K），可用于未来数据驱动的视觉时尚研究。 |
| 课程简介: | We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a large-scale annotated data set of fashion images Fashion-136K) that can be exploited for future research in data driven visual fashion. |
| 关 键 词: | 大规模数据集; 图像数据集; 视觉推荐系统 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 63 |