Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System

TL;DR

This study proposes a recommendation system that leverages user shopping history to improve prediction accuracy and shows significant improvements in both NDCG@10 and HR@10.

Abstract

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10.

Figures (3)

• Figure 1: Double column output (classfile: cas-dc.cls).
• Figure 2: The evanescent light - $1S$ quadrupole coupling ($g_{1,l}$) scaled to the bulk exciton-photon coupling ($g_{1,2}$). The size parameter $kr_{0}$ is denoted as $x$ and the is placed directly on the cuprous oxide sample ($\delta r=0$, See also Fig. \ref{['FIG:2']}).
• Figure 3: Schematic of formation of the evanescent polariton on linear chain of . The actual dispersion is determined by the ratio of two coupling parameters such as exciton- coupling and - coupling between the microspheres.