A big data based product ranking solution

Jinfeng Li, B. Shao, Jian Xu, Hongliang Li, Qinghua Wang
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引用次数: 2

Abstract

Users' online behavior, generated from endpoints of e-commerce website and app, is regarded as big data which can create large business value by mining them to acquire insights of users' preference, inclination and purpose. A good ranking result of product search and product assortment in online category classification can lift user's click rate, increase purchasing conversion rate and improve customer online experience. In this paper, we will introduce an online product based learning to rank model to intelligently learn product ranking. A big data architecture will also be introduced to implement this learning to rank model which analyzes huge amount of users' online behavior.
基于大数据的产品排名解决方案
从电子商务网站和应用端产生的用户上网行为被视为大数据,通过挖掘这些数据,可以洞察用户的偏好、倾向和目的,从而创造巨大的商业价值。良好的产品搜索排名结果和在线品类分类中的产品分类可以提高用户的点击率,增加购买转化率,改善客户的在线体验。在本文中,我们将引入一种基于在线产品排名学习的模型来智能学习产品排名。我们还将引入一个大数据架构来实现这个学习排名模型,该模型可以分析大量用户的在线行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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