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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150795

Title:	Reinforcement Learning-based Livestreaming E-commerce Recommendation System
Authors:	蕭舜文 Hsiao, Shun-Wen;Lin, Yi-Ling;Tang, Szu-Chi
Contributors:	資管系
Keywords:	Data Science and Machine Learning to Support Business Decisions;exploitation-exploration trade-off;livestreaming e-commerce;reinforcement learning;uncertainty
Date:	2024-01
Issue Date:	2024-04-11 09:45:03 (UTC+8)
Abstract:	Unlike conventional commerce, livestreaming e-commerce continuously introduces new products, resulting in a dynamic and complex context. To address the trade-off between exploration and exploitation in such a rapidly evolving recommendation context, we propose a reinforcement learning-based solution focusing on the relationships between customers, streamers, and products. We apply RNN to model the context changes in users’ preferences for streamers and products while maintaining long-term engagement. The proposed livestreaming e-commerce recommendation system (LERS) enhances the exploration of new items by incorporating uncertainty into neural networks through VAE for user modeling and BNN for product recommendation. We conducted comparisons between LERS and multi-armed bandit algorithms using real-world business data. Our findings support the proposed theoretical framework and highlight the potential practical applications of our algorithm.
Relation:	Hawaii International Conference on System Sciences, AIS
Data Type:	conference
Appears in Collections:	[Department of MIS] Proceedings

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