A framework for approximate product search using faceted navigation and user preference ranking

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.