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. They, however, 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.
翻译:产品搜索是客户在网上发现产品最受欢迎的方法之一。 大多数关于产品搜索的现有研究都侧重于开发有效的检索模型,按购买的可能性排列项目。 但是,它们忽略了系统与客户之间如何看待项目相关性存在差距的问题。 没有解释, 用户可能不明白为什么产品搜索引擎为它们检索某些项目, 从而导致用户经验不完善和系统性能不优化。 在这项工作中, 我们通过为产品搜索建立可解释的检索模型来解决这个问题。 具体地说, 我们提议将“ 搜索和购买” 行为作为用户和项目之间的动态关系模型, 并基于多关系产品数据和搜索会场的背景来创建动态的知识图表。 排序是根据用户与潜在空间中项目之间的关系进行的, 解释是用逻辑推论和知识图上的实体软匹配来产生的。 实证实验显示, 我们称之为动态内嵌模型的模型( DREM), 大大超越了状态基线和搜索结果的合理解释能力。