JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT

Duan, Sijing; Zhang, Deyu; Wang, Yan‐Bo; Li, Lingxiang; Zhang, Yaoxue

doi:10.1109/jiot.2019.2944889

Cited by 51 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation results verify the feasibility of the proposed algorithm with reduced latency and privacy enhancement in various network settings. Similarly, the study in [57] also considers an FL scheme in a mobile IoT network consisting of cloud servers and mobile devices as learning clients. The potential of FL in mobile cloud is investigated via a video recommendation system where each cloud collaboratively trains a local FL algorithm enabled by a dual-convolutional probabilistic matrix factorization model with user profile and textual information of videos as non-IID datasets.…”

Section: A Fl Serving As An Alternative To Iot Data Sharingmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

View full text Add to dashboard Cite

Section: A Fl Serving As An Alternative To Iot Data Sharingmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

View full text Add to dashboard Cite

“…The authors proposed a time-aware smart object recommendation model by jointly considering user preference over time and the smart object similarity. Duan et al [30] proposed Join-tRec, a deep learning-based joint cloud video recommendation framework for IoT; JointRec aims to provide an accurate video recommendation service to a minority of users. Huang et al [31] considered that in IoT, the description information of items is typically heterogeneous and multimodal.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective

Gao

Qin

Barroso

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

111

View full text Add to dashboard Cite

show abstract

“…The researchers projected a timeaware, bright object recommendation model integrating user interest across times and creative object resemblance. Duan et al [27] suggested JointRec, a deep learning-based joint video streaming recommendation system for IoT, to deliver precise video ways for a better marginal of users. Item descriptor information in IoT is generally diverse and multimodal, according to Huang et al [28].…”

Section: Related Workmentioning

confidence: 99%

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Ramathulasi¹,

Babu²

2023

IJACSA

View full text Add to dashboard Cite

Internet 2.0 Things connected to the Internet not only manage data supply through devices but also control the commands that flow through it. The communication technology created by the desired sensor is used by a new computing model so that the collected data appears in Web 2.0 for management. In addition to enhancing Sense efficiency through the simple IoT computing process, it is used in many cases for example video surveillance, and improved and intelligent manufacturing. Every fragment of the system is carefully continued and supervised in this process by software collection using a large number of recurs. An important process for this is to access web APIs from various public platforms in an efficient way. The use of different APIs by developers for the integration of different IoT devices and the deployment process required for this is unnecessary. Obtaining configured target APIs makes it easy to know where and how to get started with the workflow approach. Rapid industrial development can be achieved through this powerful API approach. But finding adequately powerful APIs from a large number of APIs has become a great challenge. However, due to the massive spike in the count of APIs, combining the two APIs has now become a major challenge. In this paper, for the time being, only the relationships between users and the API are considered. In this case, they had to face difficulties in extracting contextual value from their interpretation. So better accuracy could not be obtained due to this. The consequence of the user's time aspect on the cryptographic properties concerning the information collected from the API contextual description can be enhanced by the Deep Learning Probabilistic Matrix Factorization (DL-PMF) method, which improves the accuracy of the API recommendation in considering the cryptographic features of the user in the API recommendation. In this paper, we have used CNN (Convulsive Neural Network) for web elements such as APIs, and LSTM (Long-Term and Short-Term Memory) Network, which works with a diligent mechanism to find hidden features, to find hidden features that suit the tastes of the users. In conclusion, the combination of PMF (Probabilistic Matrix Factorization) evaluation of the recommended results was obtained as described above. The combination of DL-PMF method experimental results was found to be better than previous PMF, ConvMF, and other methods, thus improving the recommended accuracy.

show abstract

JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT

Cited by 51 publications

References 23 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective

Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Contact Info

Product

Resources

About