参考产品搜索
Reference Product Search
摘要：
对于感兴趣的产品，我们提出了一种搜索方法，表面的一组参考产品。参考产品可用作支持下游建模任务和业务应用的候选产品。搜索方法包括产品表示学习和指纹型矢量搜索。通过一种新颖的注意力自动编码器神经网络将产品目录信息转换为低维的高质量嵌入，并且嵌入进一步与二进制编码向量耦合以用于快速检索。我们进行了大量的实验来评估所提出的方法，并将其与对等服务进行比较，以证明其在搜索返回率和精度方面的优势。
英文摘要：
For a product of interest, we propose a search method to surface a set of reference products. The reference products can be used as candidates to support downstream modeling tasks and business applications. The search method consists of product representation learning and fingerprint-type vector searching. The product catalog information is transformed into a high-quality embedding of low dimensions via a novel attention auto-encoder neural network, and the embedding is further coupled with a binary encoding vector for fast retrieval. We conduct extensive experiments to evaluate the proposed method, and compare it with peer services to demonstrate its advantage in terms of search return rate and precision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
