One of the problems that e-commerce users face is that the desired products are sometimes not available and Web shops fail to provide similar products due to their exclusive reliance on Boolean faceted search. User preferences are also often not taken into account. In order to address these problems, we present a novel framework specifically geared towards approximate faceted search within the product catalog of a Web shop. It is based on adaptations to the p-norm extended Boolean model, to account for the domain-specific characteristics of faceted search in an e-commerce environment. These e-commerce specific characteristics are, for example, the use of quantitative properties and the presence of user preferences. Our approach explores the concept of facet similarity functions in order to better match products to queries. In addition, the user preferences are used to assign importance weights to the query terms. Using a large-scale experimental setup based on real-world data, we conclude that the proposed algorithm outperforms the considered benchmark algorithms. Last, we have performed a user-based study in which we found that users who use our approach find more relevant products with less effort.

电子商务用户面临的问题之一是有时无法获得所需的产品，并且由于完全依赖布尔分面搜索，网上商店无法提供类似的产品。用户偏好通常也没有被考虑在内。为了解决这些问题，我们提出了一个专门针对网上商店产品目录中的近似分面搜索的新颖框架。它基于对 p 范数扩展布尔模型的调整，以考虑电子商务环境中分面搜索的特定领域特征。这些电子商务特定特征例如是定量属性的使用和用户偏好的存在。我们的方法探索了方面相似性函数的概念，以便更好地将产品与查询相匹配。此外，用户偏好用于为查询项分配重要性权重。使用基于真实数据的大规模实验设置，我们得出的结论是，所提出的算法优于所考虑的基准算法。最后，我们进行了一项基于用户的研究，我们发现使用我们的方法的用户可以更轻松地找到更相关的产品。