arXiv 1806.072962018-06-19cs.IR

面向eCommerce产品搜索的端到端神经排序：任务模型和文本嵌入的应用

End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings

Eliot Brenner, Jun Zhao, Aliasgar Kutiyanawala, Zheng Yan

备注：: Accepted to appear at the SIGIR 2018 workshop on eCommerce

摘要：

我们考虑的问题，检索和排名的电子商务目录中的项目，通常被称为SKU，为了相关的用户发出的查询。用于排序的输入数据是查询的文本和目录中索引的SKU的文本字段。我们审查的方式，这个问题既相似又不同的问题，IR在网络搜索的背景下。产品搜索问题和网络搜索的IR问题之间的差异需要在模型和数据集方面采用不同的方法。我们首先回顾了最近的国家的最先进的网络搜索IR模型，区分两种不同类型的模型，我们称之为分布式和本地交互型。不同类型的相关性模型开发的IR有互补的优点和缺点时，应用于电子商务产品搜索。此外，我们解释了为什么在IR文献中使用的数据集构建的传统方法无法产生足以用于电子商务产品搜索模型的训练或评估的数据。我们解释了我们自己的方法，将任务建模技术应用于电子商务网站的点击日志，可以构建一个大规模的数据集，用于相关性模型的训练和鲁棒基准测试。我们的实验包括将IR文献中的几个模型应用于我们自己的数据集。根据经验，我们已经确定，当应用于我们的数据集时，与基线tf-idf相比，某些局部交互类型的模型将排名错误减少了三分之一。应用于我们的数据集，分布式模型未能超越基线。作为部署系统的基础，分布式模型在计算上比本地交互模型有几个优点。这激发了一个正在进行的工作计划，我们在论文的结尾处概述了这个计划。

英文摘要：

We consider the problem of retrieving and ranking items in an eCommerce catalog, often called SKUs, in order of relevance to a user-issued query. The input data for the ranking are the texts of the queries and textual fields of the SKUs indexed in the catalog. We review the ways in which this problem both resembles and differs from the problems of IR in the context of web search. The differences between the product-search problem and the IR problem of web search necessitate a different approach in terms of both models and datasets. We first review the recent state-of-the-art models for web search IR, distinguishing between two distinct types of model which we call the distributed type and the local-interaction type. The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search. Further, we explain why the conventional methods for dataset construction employed in the IR literature fail to produce data which suffices for training or evaluation of models for eCommerce product search. We explain how our own approach, applying task modeling techniques to the click-through logs of an eCommerce site, enables the construction of a large-scale dataset for training and robust benchmarking of relevance models. Our experiments consist of applying several of the models from the IR literature to our own dataset. Empirically, we have established that, when applied to our dataset, certain models of local-interaction type reduce ranking errors by one-third compared to the baseline tf-idf. Applied to our dataset, the distributed models fail to outperform the baseline. As a basis for a deployed system, the distributed models have several advantages, computationally, over the local-interaction models. This motivates an ongoing program of work, which we outline at the conclusion of the paper.

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。

摘要：

英文摘要：

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