Web Personalization Models using Computational Intelligence
Web Page recommendation Models
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Web Personalization systems have been steadily gaining ground as essential components of today s web based applications in providing support for web search and navigation, and hence pose a great challenge of information overloading. Hence efficient and intelligent techniques are needed to mine web data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' experience. The important computational intelligence methods for personalization of web-based systems are Fuzzy Systems, Rough sets, Genetic algorithms, and Swarm intelligence techniques. This book fo...
Web Personalization systems have been steadily gaining ground as essential components of today s web based applications in providing support for web search and navigation, and hence pose a great challenge of information overloading. Hence efficient and intelligent techniques are needed to mine web data for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' experience. The important computational intelligence methods for personalization of web-based systems are Fuzzy Systems, Rough sets, Genetic algorithms, and Swarm intelligence techniques. This book focuses on computational intelligent tools like rough sets, fuzzy sets and artificial intelligent techniques like ant clustering techniques to obtain user models. A key issue for page recommendation is the inherent uncertainty about what individual users will be seeking when they begin interacting with the web site. In order to alleviate the uncertainty, soft computing models are proposed based on Fuzzy clustering and Fuzzy biclustering. In order to handle outliers and overlapping components, Rough set based clustering and biclustering models are proposed.
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