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.</p

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.</p

A certainty-based approach for dynamic hierarchical classification of product order satisfaction

E-commerce companies collaborate with retailers to sell products via their platforms, making it increasingly important to preserve platform quality. In this paper, we contribute by introducing a novel method to predict the quality of product orders shortly after they are placed. By doing so, platforms can act fast to resolve bad quality orders and potentially prevent them from happening. This introduces a trade-off between accuracy and timeliness, as the sooner we predict, the less we know about the status of a product order and, hence, the lower the reliability. To deal with this, we introduce the Hierarchical Classification Over Time (HCOT) algorithm, which dynamically classifies product orders using top-down, non-mandatory leaf-node prediction. We enforce a blocking approach by proposing the Certainty-based Automated Thresholds (CAT) algorithm, which automatically computes optimal thresholds at each node. The resulting CAT-HCOT algorithm has the ability to provide both accurate and timely predictions by classifying a product order's quality on a daily basis if the classification reaches a predefined certainty. CAT-HCOT obtains a predictive accuracy of 94%. Furthermore, CAT-HCOT classifies 40% of product orders on the order date itself, 80% within five days after the order date, and 100% of product orders after 10 days.</p

A certainty-based approach for dynamic hierarchical classification of product order satisfaction

E-commerce companies collaborate with retailers to sell products via their platforms, making it increasingly important to preserve platform quality. In this paper, we contribute by introducing a novel method to predict the quality of product orders shortly after they are placed. By doing so, platforms can act fast to resolve bad quality orders and potentially prevent them from happening. This introduces a trade-off between accuracy and timeliness, as the sooner we predict, the less we know about the status of a product order and, hence, the lower the reliability. To deal with this, we introduce the Hierarchical Classification Over Time (HCOT) algorithm, which dynamically classifies product orders using top-down, non-mandatory leaf-node prediction. We enforce a blocking approach by proposing the Certainty-based Automated Thresholds (CAT) algorithm, which automatically computes optimal thresholds at each node. The resulting CAT-HCOT algorithm has the ability to provide both accurate and timely predictions by classifying a product order's quality on a daily basis if the classification reaches a predefined certainty. CAT-HCOT obtains a predictive accuracy of 94%. Furthermore, CAT-HCOT classifies 40% of product orders on the order date itself, 80% within five days after the order date, and 100% of product orders after 10 days.</p