Enhancing E-commerce with Hierarchical Recommendation System

Konsep Inti

Enriching user shopping history improves recommendation accuracy in e-commerce platforms.

Abstrak

The content discusses the importance of enriching user shopping history for accurate recommendations in e-commerce platforms. It introduces a hierarchical recommendation system that leverages bidirectional encoder representations to predict missing parts of a user's shopping history and improve recommendation accuracy. The system shows significant improvements in NDCG@10 and HR@10 metrics. The paper also compares the proposed approach with existing baseline models and evaluates its performance across different datasets.
Structure:

Introduction to Recommendation Systems in E-commerce Platforms
Traditional vs Deep Learning-based Approaches
Proposed Hierarchical Recommendation System Architecture
Evaluation Strategy & Metrics (NDCG@10, HR@10)
Results & Discussions on Test Scenarios
Conclusion and Future Work

Statistik

A richer user history results in more accurate recommendations.
The proposed approach shows significant improvements in both NDCG@10 and HR@10.

Kutipan

"The proposed approach predicts the item the user may have purchased for a given position using the previous and subsequent items."
"Our recommendation system leverages user shopping history to improve prediction accuracy."

Wawasan Utama Disaring Dari

Enriching User Shopping History

by Irem Islek,S... pada arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12096.pdf

Pertanyaan yang Lebih Dalam

How can the hierarchical recommendation system be applied to cross-domain recommendations

The hierarchical recommendation system can be applied to cross-domain recommendations by leveraging the enriched user shopping history across different domains. In this scenario, the system would enrich the user's shopping history with relevant items from various domains or platforms. By incorporating data from multiple sources and domains, the recommendation system can provide more comprehensive and personalized recommendations to users who engage with diverse products or services. This approach enhances the system's ability to understand user preferences and behaviors across different domains, leading to more accurate cross-domain recommendations.

What are the implications of placing imaginary masks between shopping sessions for improving recommendation performance

Placing imaginary masks between shopping sessions has significant implications for improving recommendation performance in several ways:

Enhanced User Understanding: By placing imaginary masks between shopping sessions, the model captures potential gaps in a user's shopping behavior that may not be evident when considering only sequential interactions within a session.
Improved Personalization: Enriching the user's shopping history with imaginary masks between sessions allows for a more nuanced understanding of their preferences and interests across different contexts or timeframes.
Increased Recommendation Accuracy: The placement of imaginary masks at strategic points helps predict missing items accurately, leading to better next-item recommendations based on an enriched shopping history.
Realistic Simulation: Mimicking real-world scenarios where users shop from multiple platforms by adding masks between sessions provides a more realistic representation of user behavior, resulting in improved recommendation performance.

How can real-world e-commerce platforms benefit from automatically enriching users' shopping history

Real-world e-commerce platforms can benefit significantly from automatically enriching users' shopping history in several ways:

Personalized Recommendations: Automatic enrichment of users' shopping histories enables e-commerce platforms to tailor recommendations based on individual preferences and past interactions accurately.
Improved User Engagement: By providing more relevant and personalized product suggestions derived from enriched histories, platforms can enhance user engagement and satisfaction.
Enhanced Conversion Rates: A deeper understanding of users' preferences through enriched histories can lead to higher conversion rates as tailored recommendations are likely to resonate better with customers.
Competitive Advantage: E-commerce platforms that leverage automatic enrichment of user data gain a competitive edge by offering superior personalized experiences compared to those relying solely on basic historical information.
Optimized Marketing Strategies: Insights derived from enriched shopping histories enable targeted marketing campaigns that are aligned with individual customer needs and behaviors, leading to higher ROI on marketing efforts.

These benefits collectively contribute towards creating a more dynamic and effective e-commerce ecosystem that prioritizes personalization and customer-centric strategies based on enriched historical data analysis.