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A personalized recommendation system is developed from the user’s search by (Liang et al., 2008) to provide customized content. Similarly, news categorization is made personally to the target users of different groups with scalable document classification techniques (Ioannis et al., 2006).
Collaborative tagging improves the keyword extraction process with better outcomes. Content-based tagging system represents the capabilities of search systems (Nirmala et al., 2010). The personalized blog recommendation system developed (Chiu et al., 2018) for mobile phone users. User history and browsing content are analyzed to provide targeted recommendations.
A personalized web-bot is created to assist the user based on their interest to view specific content and webpages (Jung et al., 2004). This system is developed by fusing collaborative filtering and hybrid content-based filtering techniques. Web browsing classification system on mobile interfaces is developed with six standard perspectives (Roudaki et al., 2015).
Genetic algorithm based document clustering method is proposed to mine the text from a large amount of biomedical information (Wahiba et al., 2016). A structured meta-data extraction method is deployed to fetch information from scientific studies (Tkaczyk et al., 2015) and available for researchers under open source license.