The Use of Media Characteristics and User Behavior for the Design of Multimedia Servers

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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Session details: Session 8A: User Behavior and Experience

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR'19 ◽

10.1145/3331184.3349694 ◽

2019 ◽

Keyword(s):

User Behavior

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Embedded task system and Gaussian mixture model in the analysis and application of user behavior in marketing management

Wireless Networks ◽

10.1007/s11276-021-02697-w ◽

2021 ◽

Author(s):

Xia Wang

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

User Behavior ◽

Gaussian Mixture ◽

Marketing Management ◽

Task System

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Personalized project recommendations: using reinforcement learning

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1619-6 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 1

Author(s):

Faxin Qi ◽

Xiangrong Tong ◽

Lei Yu ◽

Yingjie Wang

Keyword(s):

Reinforcement Learning ◽

User Behavior ◽

Collaborative Work ◽

Recursive Least Squares ◽

The Internet ◽

Dynamic Impact ◽

Rls Algorithm ◽

Trust Value ◽

Q Learning ◽

Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

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