Prefetching of VoD Programs Based On ART1 Requesting Clustering

TL;DR

This paper introduces a neural network-based clustering method to predict and prefetch VoD content, significantly improving streaming server performance by adapting to changing user request patterns.

Contribution

It presents a novel ART1 neural network clustering approach for VoD user requests that enables effective prefetching and adapts to evolving user preferences.

Findings

Enormous increase in streaming server performance.

Effective unsupervised clustering of user requests.

Adaptive prefetching based on user request patterns.

Abstract

In this paper, we propose a novel approach to group users according to the VoD user request pattern. We cluster the user requests based on ART1 neural network algorithm. The knowledge extracted from the cluster is used to prefetch the multimedia object from each cluster before the users request. We have developed an algorithm to cluster users according to the users request patterns based on ART1 neural network algorithm that offers an unsupervised clustering. This approach adapts to changes in user request patterns over period without losing previous information. Each cluster is represented as prototype vector by generalizing the most frequently used URLs that are accessed by all the cluster members. The simulation results of our proposed clustering and prefetching algorithm, shows enormous increase in the performance of streaming server. Our algorithm helps the servers agent to learn user preferences and discover the information about the corresponding sources and other similar interested individuals.