Abstract: Ant-Based Web Content Clustering based on personalization of users uses Ant-based Clustering algorithm for efficient clustering of web contents and provide the users with the best solution. Query log analysis has emerged as one of the most promising research areas to automatically derive such structures. A biologically inspired model based on Ant Colony Optimization applied to query logs as an adaptive learning process that addresses the problem of deriving query suggestions is explored. A user interacts with the ranking system at pre-computation and query times. During pre-computation, the user-specific personalized Weight Assignment Vector can be computed using (a) It can done by domain experts according to the user’s profile (b) It can be learned automatically by user interaction and exploiting user relevance feedback User interactions with the search engine which is treated as an individual ant’s journey and over time the collective journeys of all ants result in strengthening more popular paths which leads to a corresponding term association graph that is used to provide query modification suggestion which is updated in a continuous learning cycle. Ant Colony based Clustering has been studied extensively as a form of swarm intelligence technique to solve problems in several domains such as Scheduling, Classification and routing problems.
Keywords: personalization, Ant colony, WAV.
Title: Ant Based Web Content Clustering Based On Personalization of Users
Author: Preethi.C, K.Bhuvaneswari, G.Sudhakar
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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