SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine Intelligence
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48984-6_1
Reference18 articles.
1. Elahi, M., Hosseini, R., Rimaz, M.H., Moghaddam, F.B., Trattner, C.: Visually-aware video recommendation in the cold start. In: Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 225–229 (2020)
2. Du, X., Yin, H., Chen, L., Wang, Y., Yang, Y., Zhou, X.: Personalized video recommendation using rich content from videos. IEEE Trans. Knowl. Data Eng. 32(3), 492–505 (2018)
3. Zhu, H., et al.: A cross-curriculum video recommendation algorithm based on a video-associated knowledge map. IEEE Access 6, 57562–57571 (2018)
4. Vimala, S.V., Vivekanandan, K.: A Kullback-Leibler divergence-based fuzzy C-means clustering for enhancing the potential of a movie recommendation system. SN Appl. Sci. 1, 1–11 (2019)
5. Yan, W., Wang, D., Cao, M., Liu, J.: Deep auto-encoder model with convolutional text networks for video recommendation. IEEE Access 7, 40333–40346 (2019)
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