Digital Media Visual Recommendation System Based on Artificial Neural Network Machine Learning
DOI:
https://doi.org/10.13052/jmm1550-4646.2156Keywords:
Recommendation system, deep learning, CNN-BiLSTM, multi-head attention, visualization, short videoAbstract
Conventional collaborative filtering methods struggle to understand the subtle connections between users and evolving short video content, often resulting in imprecise or broad recommendations. Additionally, the absence of interpretability, usually displayed as unchanging lists or tables, restricts user confidence and involvement. To overcome these challenges, this research suggests a tailored short video recommendation system that integrates a CNN-BiLSTM hybrid model with a Multi-Head Attention (MHA) approach and visualization techniques. CNN is utilized to obtain visual characteristics from brief videos, BiLSTM identifies temporal relationships in video sequences and user actions, while MHA improves feature weighting for tailored significance. To address the transparency concern, the system incorporates real-time visualization methods like heat maps and interactive charts, enabling users to grasp the reasoning behind each suggestion. Experimental findings from the MicroLens dataset indicate that the proposed model achieves a hit rate of 0.94 at k =15, outperforming conventional methods such as ItemCF by 0.16. This method greatly enhances the accuracy of recommendations, transparency, and user engagement in digital media contexts
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L. Wang, G. Che, J. Hu, L. Chen, “Online review helpfulness and information overload: The roles of text, image, and video elements,” Proc. J. Theor. Appl. Electron. Commer. Res., vol. 19, no. 2, pp. 1243–1266, 2024.
Z. Zhou, T. Pan, X. Li, “Restricted use of social media: A temporal view of overload change and the contingency of prominence,” Proc. Int. J. Inf. Manag., vol. 11, no. 2, pp. 115–132, 2024.
L. Shahrzadi, A. Mansouri, M. Alavi, A. Shabani, “Causes, consequences, and strategies to deal with information overload: A scoping review,” Proc. Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, pp. 11–22, 2024.
H. Razgallah, M. Vlachos, A. Ajalloeian, N. Liu, J. Schneider, A. Steinmann, “Using neural and graph neural recommender systems to overcome choice overload: Evidence from a music education platform,” Proc. ACM Trans. Inf. Syst., vol. 42, no. 4, pp. 1–26, 2024.
W. Liu, H. Wan, B. Yan, “Short video recommendation algorithm incorporating temporal contextual information and user context,” Proc. CMES-Comput. Model. Eng. Sci., vol. 135, no. 1, pp. 239–258, 2023.
Y. Xiang, S. Huo, Y. Wu, Y. Gong, M. Zhu, “Integrating AI for enhanced exploration of video recommendation algorithm via improved collaborative filtering,” Proc. J. Theory Pract. Eng. Sci., vol. 4, no. 2, pp. 83–90, 2024.
G. Parthasarathy, S. S. Devi, “Hybrid recommendation system based on collaborative and content-based filtering,” Proc. Cybern. Syst., vol. 54, no. 4, pp. 432–453, 2023.
H. I. Abdalla, A. A. Amer, Y. A. Amer, L. Nquyen, B. Al-Maqaleh, “Boosting the item-based collaborative filtering model with novel similarity measures,” Proc. Int. J. Comput. Intell. Syst., vol. 16, no. 1, pp. 123–138, 2023.
A. Torkashvand, S. M. Jameii, A. Reza, “Deep learning-based collaborative filtering recommender systems: A comprehensive and systematic review,” Proc. Neural Comput. Appl., vol. 35, no. 35, pp. 24783–24827, 2023.
Z. Zhou, L. Zhang, N. Yang, “Contrastive collaborative filtering for cold-start item recommendation,” Proc. ACM Web Conf. (WWW), pp. 3121–3130, 2023. Available: https://arxiv.org/abs/2302.02151.
M. Kong, L. Fan, “Collaborative filtering in latent space: A Bayesian approach for cold-start music recommendation,” Proc. Pacific-Asia Conf. Knowl. Discov. Data Min. (PAKDD), pp. 107–119, 2024. Available: https://link.springer.com/chapter/10.1007/978-981-97-2262-4_9.
W. Wei, C. Huang, L. Xia, C. Zhang, “Multi-modal self-supervised learning for recommendation,” Proc. arXiv preprint, arXiv:2302.10632, 2023. Available: https://arxiv.org/abs/2302.10632.
J. Hu, B. Liu, Y. Xu, Z. Zhang, Y. Liu, “BiVRec: Bidirectional view-based multimodal sequential recommendation,” Proc. arXiv preprint, arXiv:2402.17334, 2024. Available: https://arxiv.org/abs/2402.17334.
ACM Multimedia, “Multi-view graph convolutional network for multimedia recommendation,” Proc. ACM Int. Conf. Multimedia (MM), pp. 7252–7260, 2023. Available: https://dl.acm.org/doi/abs/10.1145/3581783.3613915.
J. Liu, Z. Fang, Y. He, “AttnCF: Attention-based collaborative filtering with user intent visualization,” Proc. ACM Trans. Recomm. Syst., vol. 2, no. 1, Article 6, 2024.
J. Gou, J. Guo, L. Zhang, C. Wang, F. C. Wu, “Collaborative filtering recommendation system based on trust-aware and domain experts,” Proc. Informatics Des. Anal., vol. 17, no. 2, pp. 85–94, 2019.
Y. Chen, C. Zhang, Z. Liu, “Wasserstein collaborative filtering for item cold-start recommendation,” Proc. ACM Conf. User Modeling, Adaptation and Personalization (UMAP), pp. 24–32, 2020. Available: https://dl.acm.org/doi/10.1145/3340631.3394870.
K. V. Dudekula, H. Syed, M. I. M. Basha, S. I. Swamykan, P. P. Kasaraneni, Y. V. P. Kumar, “Convolutional neural network-based personalized program recommendation system for smart television users,” Proc. Sustainability, vol. 15, no. 3, pp. 2206–2223, 2023.
A. Nilla, E. B. Setiawan, “Film recommendation system using content-based filtering and the convolutional neural network (CNN) classification methods,” Proc. J. Ilmiah Tek. Elektro Komput. Inform. (JITEKI), vol. 10, no. 1, pp. 17–29, 2024.
D. Nor El-Deen, R. S. El-Sayed, A. Hussein, M. Zaki, “Sentiment analysis for movie recommendations: Harnessing opinion mining systems to analyze user reviews,” Proc. Egypt. Stat. J., vol. 68, no. 1, pp. 1–14, 2024.
J. Duan, P. F. Zhang, R. Qiu, Z. Huang, “Long short-term enhanced memory for sequential recommendation,” Proc. World Wide Web, vol. 26, no. 2, pp. 561–583, 2023.
R. Ayyadurai, “Advanced recommender system using hybrid clustering and evolutionary algorithms for e-commerce product recommendations,” Proc. Int. J. Manag. Res. Bus. Strategy, vol. 11, no. 1, pp. 17–27, 2021.
M. V. Devarajan, C. Solutions, “An improved BP neural network algorithm for forecasting workload in intelligent cloud computing,” Proc. J. Current Sci., vol. 10, no. 3, pp. 45–60, 2022.
A. R. G. Yallamelli, A. Sambas, “An optimized case-based reasoning approach with MAML and K-Means clustering for AI-driven multi-class workload prediction in autonomic cloud databases and data warehouse systems,” Unpublished Manuscript, 2023.
K. Parthasarathy, “Enhanced case-based reasoning with hybrid clustering and evolutionary algorithms for multi-class workload forecasting in autonomic database systems,” Proc. Int. J. HRM Organ. Behav., vol. 11, no. 2, pp. 38–54, 2023.
K. Parthasarathy, V. R. Prasaath, “Cloud-based deep learning recommendation systems for personalized customer experience in e-commerce,” Proc. Int. J. Appl. Sci. Eng. Manag., vol. 12, no. 2, 2018.
B. Bakariya, A. Singh, H. Singh, R. Rajpoot, K. K. Mohbey, “Facial emotion recognition and music recommendation system using CNN-based deep learning techniques,” Proc. Evol. Syst., vol. 15, no. 2, pp. 641–658, 2024.
M. Alrashidi, A. Selamat, R. Ibrahim, H. Fujita, “Social recommender system based on CNN incorporating tagging and contextual features,” Proc. J. Cases Inf. Technol. (JCIT), vol. 26, no. 1, pp. 1–20, 2024.
K. N. Asha, R. Rajkumar, “DCF-MLSTM: A deep security content-based filtering scheme using multiplicative BiLSTM for movie recommendation system,” Proc. Int. J. Syst. Syst. Eng., vol. 13, no. 1, pp. 66–82, 2023.
