Multi-trends Enhanced Dynamic Micro-video Recommendation
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8850-1_35
Reference23 articles.
1. Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)
2. Baluja, S., et al.: Video suggestion and discovery for youtube: taking random walks through the view graph. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, 21–25 April 2008 (2008)
3. Boim, R., Milo, T., Novgorodov, S.: Diversification and refinement in collaborative filtering recommender. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 739–744. Association for Computing Machinery, New York (2011)
4. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering (2013)
5. Chen, B., Wang, J., Huang, Q., Mei, T.: Personalized video recommendation through tripartite graph propagation. In: Proceedings of the 20th ACM Multimedia Conference, MM 2012, Nara, Japan, 29 October–02 November 2012 (2012)
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