Conditioned Variational Autoencoder for Top-N Item Recommendation
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-15931-2_64
Reference25 articles.
1. Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 191–226. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_6
2. Askari, B., Szlichta, J., Salehi-Abari, A.: Variational autoencoders for top-k recommendation with implicit feedback. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2061–2065 (2021)
3. Carraro, T., Polato, M., Aiolli, F.: A look inside the black-box: towards the interpretability of conditioned variational autoencoder for collaborative filtering. In: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 233–236 (2020)
4. Chae, D.K., Kang, J.S., Kim, S.W., Lee, J.T.: Cfgan: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM 2018, New York, NY, USA, pp. 137–146. Association for Computing Machinery (2018). https://doi.org/10.1145/3269206.3271743
5. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Hierarchical Constrained Variational Autoencoder for interaction-sparse recommendations;Information Processing & Management;2024-05
2. Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer;Lecture Notes in Computer Science;2024
3. Overcoming Recommendation Limitations with Neuro-Symbolic Integration;Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14
4. An improved autoencoder for recommendation to alleviate the vanishing gradient problem;Knowledge-Based Systems;2023-03
5. Logic Tensor Networks for Top-N Recommendation;AIxIA 2022 – Advances in Artificial Intelligence;2023
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3