论文标题
关节图：在电子商务搜索中建模意图层次结构的联合查询意图理解
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
论文作者
论文摘要
对用户查询意图的准确理解可以帮助提高下游任务的性能，例如查询范围范围和排名。在电子商务领域中，查询理解的最新工作集中于对产品类别映射的查询。但是，很小一部分但很大一部分的查询（在我们的网站中，2019年的查询为1.5％或3300万查询）具有与之相关的非商业意图。 These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.关节图模型通过利用通过联合学习过程在这两个相关任务之间存在的转移偏差来起作用。由于为这些任务策划标记的数据集可能是昂贵且耗时的，因此我们提出了一种遥远的监督方法以及主动学习模型，以生成高质量的培训数据集。为了证明关节图的有效性，我们使用从大型商业网站收集的搜索查询。我们的结果表明，关节图显着改善了“商业与非商业”意图预测和产品类别映射的平均映射，平均比最先进的深度学习方法提高了2.3％和10％。我们的发现提出了一个有希望的方向，可以在电子商务搜索引擎中建模意图层次结构。
An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.
