AI in Video Recommendation System
DOI:
https://doi.org/10.54097/hset.v35i.7214Keywords:
Artificial Intelligence; Video Recommendation Systems; Neural Networks; Collaborative Filtering; Deep Learning; Datasets; Experimental Results; Research Directions.Abstract
Short videos are very popular all over the world. Video recommendation system is an essential part in it. It can help people to watch the video that they are interested in. This paper is written for study the specific principle of the video recommendation system. The result was getting through relative literatures and actual test. Short video recommendation systems typically use collaborative filtering and deep learning techniques to achieve this. Collaborative filtering comes in two types: user-based and content-based. User-based collaborative filtering recommends videos to new users based on the viewing behavior of similar users. Content-based collaborative filtering uses video features and similarity to recommend similar videos. Finally, this paper shows how the video web set can learn what is the user’s interest.
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References
Wu, Liwei, et al. “Deep Neural Networks for YouTube Recommendations.” Proceedings of the 2016 ACM Conference on Multimedia Conference, ACM, 2016, pp. 165-174.
He, Xiangnan, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. "Neural Collaborative Filtering." In Proceedings of the 26th International Conference on World Wide Web, pp. 173-182, 2017.
Guo, H., Tang, R., Ye, Y. et al. (2017). DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining pp. 627-635.
Lee, Joonseok, et al. "Deep Neural Networks for YouTube Recommendations." ACM Transactions on the Web, vol. 12, no. 1, 2018, pp. 1-22.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
