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长尾风格：利用潜在变量模型发现大规模社会电子商务中的独特利益Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce |
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| 课程网址: | http://videolectures.net/kdd2014_hu_social_ecommerce/ |
| 主讲教师: | Diane J. Hu |
| 开课单位: | Etsy公司 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  许多产品类别中的购买决定在很大程度上受到购物者审美偏好的影响。仅仅将购物者与相关类别的热门商品相匹配是不够的；成功的购物体验还可以识别出符合这些美学要求的产品。随着市场规模和多样性的增加，捕捉购物者风格的挑战变得更加困难。在Etsy（一个手工和古董商品的在线市场，拥有超过3000万种商品）上，捕捉味道的问题尤其重要，用户特别要找到适合其折衷风格的商品。 在本文中，我们描述了在Etsy网站上部署两个基于新样式的推荐系统的方法和实验。我们使用潜在Dirichlet分配（LDA）在Etsy上发现趋势类别和样式，然后将其用于描述用户的“兴趣”配置文件。我们还探索了散列方法，以在地图归约框架上执行快速最近邻居搜索，以便有效地获得推荐。这些技术已经大规模成功实施，大大改善了许多关键业务指标。 |
| 课程简介: | Purchasing decisions in many product categories are heavily influenced by the shopper's aesthetic preferences. It's insufficient to simply match a shopper with popular items from the category in question; a successful shopping experience also identifies products that match those aesthetics. The challenge of capturing shoppers' styles becomes more difficult as the size and diversity of the marketplace increases. At Etsy, an online marketplace for handmade and vintage goods with over 30 million diverse listings, the problem of capturing taste is particularly important -- users come to the site specifically to find items that match their eclectic styles. In this paper, we describe our methods and experiments for deploying two new style-based recommender systems on the Etsy site. We use Latent Dirichlet Allocation (LDA) to discover trending categories and styles on Etsy, which are then used to describe a user's "interest" profile. We also explore hashing methods to perform fast nearest neighbor search on a map-reduce framework, in order to efficiently obtain recommendations. These techniques have been implemented successfully at very large scale, substantially improving many key business metrics. |
| 关 键 词: | 电子商务; 变量模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-07：zyk |
| 最后编审: | 2020-12-07：zyk |
| 阅读次数: | 23 |