Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. However, they ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the “search and purchase” behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.

中文翻译：

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具有动态关系嵌入模型的可解释产品搜索

产品搜索是客户在线发现产品最流行的方法之一。大多数现有的关于产品搜索的研究都集中在开发有效的检索模型上，这些模型根据被购买的可能性对物品进行排名。然而，他们忽略了系统和客户如何看待项目的相关性之间存在差距的问题。如果没有解释，用户可能无法理解为什么产品搜索引擎会为他们检索某些项目，从而导致用户体验不完美和​​系统性能在实践中不理想。在这项工作中，我们通过为产品搜索构建可解释的检索模型来解决这个问题。具体来说，我们建议将“搜索和购买”行为建模为用户和物品之间的动态关系，并基于多关系产品数据和搜索会话的上下文创建动态知识图谱。根据潜在空间中用户与物品的关系进行排序，并在知识图谱上通过逻辑推理和实体软匹配生成解释。经验实验表明，我们的模型，我们称之为动态关系嵌入模型 (DREM)，显着优于最先进的基线，并且能够对搜索结果产生合理的解释。