Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Lin, Yi-Ling; Hsiao, Shun-Wen; and Tang, Szu-Chi, "Reinforcement Learning-based Livestreaming E-commerce Recommendation System" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 9.
https://aisel.aisnet.org/hicss-57/da/data_science/9
Reinforcement Learning-based Livestreaming E-commerce Recommendation System
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/da/data_science/9