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CONTEXTUAL ONLINE LEARNING FOR MULTIMEDIA

CONTENT AGGREGATION

ABSTRACT

            In this paper we study about last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources. Faced with a variety of content choices, consumers are Exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumer demand for such diverse content, multimedia content aggregators (CAs) have emerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumer preferences, contexts, and content characteristics. We propose a novel, distributed, online multimedia content aggregation framework, which gathers content generated by multiple heterogeneous producers to fulfill its consumer demand for content. Since both the multimedia content characteristics and the consumer preferences and contexts are unknown, the optimal content aggregation strategy is unknown a priori. Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed. Importantly, our algorithms operate efficiently even when feedback from consumers is missing or content and preferences evolve over time.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- In the existing system the server cluster handles only a specific type of multimedia task, and each client requests a different type of multimedia service at a different time. Such a scenario can be modeled as an integer linear programming problem.	PROPOSED CONCEPT:- In the proposed system a cloud computing based Framework for big data information management in smart grids, which provides not only flexibility and scalability but also security features.
EXISTING ALGORITHM:- centralized algorithm	PROPOSED ALGORITHM:- Aggregation algorithm and Learning algorithms
ALGORITHM DEFNITION:- Centralized algorithms in the distributed implementation of these centralized algorithms, we assume that each CA runs an independent instance of these algorithms. For instance, when implementing a centralized algorithm on the distributed system of CAs.	ALGORITHM DEFNITION:- Aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed.
DRAWBACKS:- Centralized method is suitable for only small collection of system. Server cluster can handle only one type request at a time so that problem occurs.	ADVANTAGES:- The Aggregation is scalable framework is suitable for larger collection of systems. Client’s requests assigned to each server. Cluster so that the load of each server cluster is distributed as balanced.