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Web personalization helps in understanding the user interests and creating customized experiences for users. However the user preferences changes dynamically over a period. In order to adapt with the changing information needs of the user, we have developed a novel web personalization system that captures the user changing interest by analyzing the timing information. We use splay tree, which is a...
Crowdsourcing systems are distributed problem solving platforms, where small tasks are channelled to a crowd in the form of open calls for solutions. Reward based crowdsourcing systems tries to attract the interested and capable workers to provide solutions in return for monetary rewards. We study the task recommendation problem in reward based crowdsourcing platforms, where we leverage both implicit...
The main goal of focused web crawlers is to retrieve as many relevant pages as possible. However, most of the crawlers use page rank algorithm to lineup the pages in the crawler frontier. Since the page rank algorithm suffers from the drawback of “Richer get rich phenomenon”, focused crawlers often fail to retrieve the hidden relevant pages. This paper presents a novel approach for retrieving the...
Recommendation systems are widely used in ecommerce applications. A recommendation system intends to recommend the items or products to a particular user, based on user's interests, other user's preferences, and their ratings. To provide a better recommendation system, it is necessary to generate associations among products. Since e-commerce and social networking sites generates massive data, traditional...
Web content extraction is a popular technique for extracting the main content from web pages and discards the irrelevant content. Extracting only the relevant content is a challenging task since it is difficult to determine which part of the web page is relevant and which part is not. Among the existing web content extraction methods, density based content extraction is one popular method. However...
Peer to Peer (P2P) systems have increased the curiosity and pathways for people to discover and share the resources. While various methods have been proposed in the discovery of discrete value based resources, there is also a surging interest in discovering a range of resources for a given request. This work is a novel design of a P2P network that adheres to range requests and seeks to discover the...
The popularity of Peer-to-peer (P2P) applications have shown a massive growth in recent times, and P2P traffic contributes considerably to the today's internet traffic. For efficient network traffic management and effective malware detection, P2P traffic classification is indispensable. This paper proposes LASER, Longest Common Subsequence (LCS)-based Application Signature ExtRaction technique, algorithm,...
The key aspect in building a Web personalization system is the user's navigational pattern. However, the navigational pattern alone is insufficient to capture the user's interest and behavior. This paper proposes a novel web personalization system that accepts the timing information, semantic information along with the navigational pattern, and classifies the users according their interest and behavior...
Peer-to-Peer (P2P) traffic shows a rapid growth in recent times. For efficient malware detection and network traffic management P2P network traffic classification is essential. The existing P2P traffic classification methods includes port-based, signature-based, pattern-based, and statistics based methods. However, none of these methods proved to be effective for the traffic classification in terms...
This paper addresses the problem of cache pollution in P2P (Peer-to-peer) systems. P2P traffic has a significant impact on ISPs as it accounts for more than half of the all traffic. This will cause some negative impact on the Internet like network congestion, high latency. P2P caching is an efficient method for handling this problem. Most of the P2P cache systems suffer from Cache Pollution. This...
Web caches are useful in reducing the user perceived latencies and web traffic congestion. Multi-level classification of web objects in caching is relatively an unexplored area. This paper proposes a novel classification scheme for web cache objects which utilizes a multinomial logistic regression (MLR) technique. The MLR model is trained to classify web objects using the information extracted from...
This paper proposes a novel admission and replacement technique for web caching, which utilizes the multinomial logistic regression (MLR) as classifier. The MLR model is trained for classifying the web cache's object worthiness. The parameter object worthiness is a polytomous (discrete) variable which depends on the traffic and the object properties. Using worthiness as a key, an adaptive caching...
Multi-level classification of web objects in caching is relatively an unexplored area. This paper proposes a novel caching scheme which utilizes a multi-level class information. A MLR (Multinomial Logistics Regression) based classifier is constructed using the information from web logs. Simulation results confirm that the model has good prediction capability and suggest that the proposed approach...
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