WCMIVR: A Web 3.0 Compliant Machine Intelligence Driven Scheme for Video Recommendation
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27499-2_53
Reference24 articles.
1. Zhao, Z., et al.: Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 43–51, Sep (2019)
2. Tripathi, A., Ashwin, T.S., Guddeti, R.M.R.: EmoWare: a context-aware framework for personalized video recommendation using affective video sequences. IEEE Access 7, 51185–51200 (2019)
3. Duan, S., Zhang, D., Wang, Y., Li, L., Zhang, Y.: JointRec: a deep-learning-based joint cloud video recommendation framework for mobile IoT. IEEE Internet Things J. 7(3), 1655–1666 (2020)
4. Zhang, Z., Lin, Z., Zhao, Z., Zhu, J., He, X.: Regularized two-branch proposal networks for weakly-supervised moment retrieval in videos. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4098–4106, Oct (2020)
5. Shi, Y., Wei, Z., Ling, H., Wang, Z., Shen, J., Li, P.: Person retrieval in surveillance videos via deep attribute mining and reasoning. IEEE Trans. Multimedia 23, 4376–4387 (2021). https://doi.org/10.1109/TMM.2020.3042068
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