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

arXiv 2403.12096 ↗First posted 15 Mar 2024cs.IR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 15 Mar 2024.

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22 of 43 references.

References · 22

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10 more
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