[论文解读] Intent term selection and refinement in e-commerce queries

Saurav Manchanda, Mohit Sharma+1 more|arXiv (Cornell University)|Aug 22, 2019

Web Data Mining and Analysis参考文献 21被引用 4

一句话总结

本文提出了一种上下文感知的术语加权与查询优化方法，用于电商搜索，基于沃尔玛的查询重写日志。通过利用基于RNN的意图编码和上下文术语表征，该方法通过识别意图关键术语并建议填补词汇鸿沟的术语，提升了罕见查询的相关性，其在MRR和精确率指标上优于非上下文基线模型。

ABSTRACT

In e-commerce, a user tends to search for the desired product by issuing a query to the search engine and examining the retrieved results. If the search engine was successful in correctly understanding the user's query, it will return results that correspond to the products whose attributes match the terms in the query that are representative of the query's product intent. However, the search engine may fail to retrieve results that satisfy the query's product intent and thus degrading user experience due to different issues in query processing: (i) when multiple terms are present in a query it may fail to determine the relevant terms that are representative of the query's product intent, and (ii) it may suffer from vocabulary gap between the terms in the query and the product's description, i.e., terms used in the query are semantically similar but different from the terms in the product description. Hence, identifying the terms that describe the query's product intent and predicting additional terms that describe the query's product intent better than the existing query terms to the search engine is an essential task in e-commerce search. In this paper, we leverage the historical query reformulation logs of a major e-commerce retailer to develop distant-supervised approaches to solve both these problems. Our approaches exploit the fact that the significance of a term is dependent upon the context (other terms in the neighborhood) in which it is used in order to learn the importance of the term towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking. Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.

研究动机与目标

• 提升在历史互动数据有限的罕见或冷启动查询中的电商搜索相关性。
• 识别查询中哪些术语最准确地表达用户的商品意图，尤其是在存在噪声或歧义术语的情况下。
• 通过建议更相关、更具上下文适应性的术语，弥合用户查询与商品目录术语之间的词汇鸿沟。
• 开发一种可泛化的模型，利用查询重写模式推断意图，而无需依赖显式标注。

提出的方法

• 使用沃尔玛的历史查询重写日志，训练一种远程监督模型用于意图检测。
• 采用循环神经网络（RNN）编码器，对查询术语的上下文表征进行建模，捕捉术语重要性如何随上下文实体变化。
• 提出一种术语加权模型（CTW），基于上下文感知嵌入，为最能表达查询产品意图的术语分配更高权重。
• 开发一种查询优化模型（CQR），结合意图编码器与多标签分类器，预测原始查询中不存在的相关术语。
• 通过预测更匹配商品目录语言的术语，将术语重要性估计与词汇鸿沟解决相结合。
• 在保留的罕见查询数据集上，使用MRR和精确率指标评估模型，并与TF-IDF、FTW、VPCG和VG基线进行比较。

实验结果

研究问题

• RQ1 如何识别搜索查询中最能表达真实商品意图的术语，尤其是在查询包含模糊或噪声术语时？
• RQ2 与非上下文方法相比，引入相邻术语的上下文信息在多大程度上能提升术语加权的准确性？
• RQ3 能否利用查询重写模式，建议新的、更有效的术语，以弥合用户查询与商品目录描述之间的词汇鸿沟？
• RQ4 上下文感知模型在历史互动数据有限的罕见查询中，对提升搜索性能有多有效？

主要发现

• 上下文感知术语加权模型（CTW）在MRR和精确率指标上均优于非上下文基线方法（如TF-IDF、FTW、VPCG和VG），表现出更高的排序相关性。
• 对于查询'battery night light with timer'，CTW正确地为'night'和'light'分配最高权重，而基线方法则错误地优先考虑'timer'或'battery'。
• 查询优化模型（CQR）成功预测出上下文相关的术语，如'orbit red garden hose water nozzle'对应的'water spray nozzle'，避免了产生'spearmint'或'gum'等无关关联。
• 在'auto seat cover wonder woman'查询中，CTW正确识别出'auto'、'seat'和'cover'为关键术语，而基线方法未能有效消除歧义。
• CQR模型通过利用意图编码器理解商品类型，避免生成无关术语，而FQR基线因缺乏上下文理解，导致产生错误建议。
• 该方法在罕见查询上表现出色，传统方法因缺乏历史互动数据而失效，证实其在冷启动场景下的有效性。