Version 1
: Received: 27 September 2024 / Approved: 27 September 2024 / Online: 27 September 2024 (12:31:31 CEST)
How to cite:
Lu, J. Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning. Preprints2024, 2024092180. https://doi.org/10.20944/preprints202409.2180.v1
Lu, J. Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning. Preprints 2024, 2024092180. https://doi.org/10.20944/preprints202409.2180.v1
Lu, J. Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning. Preprints2024, 2024092180. https://doi.org/10.20944/preprints202409.2180.v1
APA Style
Lu, J. (2024). Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning. Preprints. https://doi.org/10.20944/preprints202409.2180.v1
Chicago/Turabian Style
Lu, J. 2024 "Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning" Preprints. https://doi.org/10.20944/preprints202409.2180.v1
Abstract
In the dynamic landscape of online shopping, predicting user behaviors such as clicks, cart additions, and orders is crucial for optimizing sales strategies. Traditional recommendation systems often focus on a single objective, limiting their effectiveness in a multifaceted e-commerce environment. This article proposes a multi-objective recommendation system that leverages previous events in user sessions to forecast these key metrics, addressing challenges such as imbalanced positive and negative samples, varying session lengths, and the need for effective sampling techniques. Our approach integrates LightGBM, XgBoost, and Recbole GRU4Rec through ensemble learning, combining the strengths of these models to enhance prediction performance. Extensive evaluations demonstrate that our model outperforms existing methods, offering significant improvements in accuracy and robustness. This work provides a comprehensive solution for online retailers to better predict user behaviors and optimize their sales strategies, ultimately enhancing customer satisfaction and business outcomes.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
