论文标题
“图像价值一千个功能”：可扩展的产品代表，用于录取类型的个性化
"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization
论文作者
论文摘要
我们解决了在数字商务环境中个性化查询完成的问题，在数字商务环境中，跳出率通常很高，并且反复出现的用户很少。我们通过在查询时间从产品图像中计算出的密集矢量来重点关注会议的个性化并改善标准的噪声频道模型。我们认为，基于图像的个性化表现出比替代建议（从数据可用性到业务可伸缩性）的几个优点，并为提出方法的有效性提供了定量证据和定性支持。最后，我们展示了如何使用类似商店之间的共享矢量空间来改善用户跨站点浏览的体验，从而开放了应用零射击的无监督个性化的个性化以增加转换的可能性。这将被证明与管理多个品牌和/或网站以及为在同一领域的多个客户提供服务的多租户SaaS提供商的零售团体特别相关。
We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare. We focus on in-session personalization and improve a standard noisy channel model by injecting dense vectors computed from product images at query time. We argue that image-based personalization displays several advantages over alternative proposals (from data availability to business scalability), and provide quantitative evidence and qualitative support on the effectiveness of the proposed methods. Finally, we show how a shared vector space between similar shops can be used to improve the experience of users browsing across sites, opening up the possibility of applying zero-shot unsupervised personalization to increase conversions. This will prove to be particularly relevant to retail groups that manage multiple brands and/or websites and to multi-tenant SaaS providers that serve multiple clients in the same space.
