Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909789794074624 |
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| author | Wang, Jinmeiyang Dong, Jing Zhou, Li |
| author_facet | Wang, Jinmeiyang Dong, Jing Zhou, Li |
| contents | This paper proposes the MT-DQN model, which integrates a Transformer, Temporal Graph Neural Network (TGNN), and Deep Q-Network (DQN) to address the challenges of predicting user behavior and optimizing recommendation strategies in short-video environments. Experiments demonstrated that MT-DQN consistently outperforms traditional concatenated models, such as Concat-Modal, achieving an average F1-score improvement of 10.97% and an average NDCG@5 improvement of 8.3%. Compared to the classic reinforcement learning model Vanilla-DQN, MT-DQN reduces MSE by 34.8% and MAE by 26.5%. Nonetheless, we also recognize challenges in deploying MT-DQN in real-world scenarios, such as its computational cost and latency sensitivity during online inference, which will be addressed through future architectural optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning Wang, Jinmeiyang Dong, Jing Zhou, Li Machine Learning Information Retrieval This paper proposes the MT-DQN model, which integrates a Transformer, Temporal Graph Neural Network (TGNN), and Deep Q-Network (DQN) to address the challenges of predicting user behavior and optimizing recommendation strategies in short-video environments. Experiments demonstrated that MT-DQN consistently outperforms traditional concatenated models, such as Concat-Modal, achieving an average F1-score improvement of 10.97% and an average NDCG@5 improvement of 8.3%. Compared to the classic reinforcement learning model Vanilla-DQN, MT-DQN reduces MSE by 34.8% and MAE by 26.5%. Nonetheless, we also recognize challenges in deploying MT-DQN in real-world scenarios, such as its computational cost and latency sensitivity during online inference, which will be addressed through future architectural optimization. |
| title | Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2509.12269 |