Volume 39, Number 5, October 2021
|Page(s)||1049 - 1056|
|Published online||14 December 2021|
- Leicht N, Blohm I, Leimeister J M. Leveraging the power of the crowd for software testing[J]. IEEE Software, 2017, 34(2) : 62–69. [Article] [Google Scholar]
- Cheng H T, Koc L, Harmsen J, et al. Wide & #38; deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016: 7–10 [Google Scholar]
- Guo H F, Tang R M, Ye Y M, et al. DeepFM: a factorization-machine based neural network for CTR prediction[C]//26th Int Joint Conf Artificial Intelligence, Melbourne, Australia, 2017 [Google Scholar]
- Lian J, Zhou X, Zhang F. Xdeepfm: combining explicit and implicit feature interactions for recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: 1754–1763 [Google Scholar]
- Zhang Zhengkui, Pang Weiguang, Xie Wenjing, et al. Deep learning for real-time applications: a survey[J]. Journal of Software, 2020, 31(9) : 2654–2677 [Article] (in Chinese) [Google Scholar]
- He X, Liao L, Zhang H, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web, 2018: 173–182 [Google Scholar]
- Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015: 811–820 [Google Scholar]
- Wang H, Wang N, Yeung D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015: 1235–1244 [Google Scholar]
- Zhang Shuai, Yao Lina, Xu Xiwei. AutoSVD++: an efficient hybrid collaborative filtering model via contractive auto-encoders[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017: 957–960 [Google Scholar]
- Wei J, He J, Chen K, et al. Collaborative filtering and deep learning base recommendation system for cold start items[J]. Expert Systems with Applications, 2017, 69 : 29–39. [Article] [Google Scholar]
- Fornacciari P, Guidi B, Mordonini M, et al. Guess the movie-linking facebook pages to IMDb movies[C]//Internationa Workshop on Personal Analytics and Privacy, 2017: 98–109 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.