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In this paper, we introduce one of the most prominent Machine Learning Algorithms known as the Naive Bayes Classifier. Naive Bayes is a classification algorithm with a simple learning mechanism, using Bayes’s theorem. Naive Bayes can be used to build models which are easy to understand, easy to train, and easy to use (Mitchell 2003). In this work, we introduce a new machine learning technique to be used to resolve problems in search engine optimization (SEO). An early research study by Tang et al. developed an SEO recommendation engine based on the three classes of web pages: (1) pages found in the search results; (2) pages not found in the search results; and (3) pages that were not found both in the search results and were not returned in any repeatable searches. Like Google, this study provides a probability score to every web document reflecting the degree of association between the candidate and the target search keyword. The association probabilities were derived from a combination of two machine learning algorithms: Naive Bayes and logistic regression with naïve Bayes assigned to generate the conditional probability. The Naive Bayes classifier was used to distinguish documents that contained the keyword, and logistic regression was used to express the degree of association between the two. In this paper, we propose to use a modified version of Naive Bayes to classify web pages according to their quality. For instance, web pages that are not well ranked in search engines, will be classified as untrustworthy web pages. Furthermore, web pages that are very well ranked in search engines will be classified as trustworthy web pages. We argue that if the keyword of a web page is popular among users, it means that the web page is trustworthy. On the other hand, when users do not search for a particular keyword, i.e. when users are not interested in this type of web pages, the web page should be classified as untrustworthy. Web pages with no content, with a high number of outbound links, or containing a few of the target keywords will be classified as untrustworthy. Finally, web pages containing a lot of the keyword and having few outbound links will be classified as trustworthy. To sum up, the classification function maps each web page into two possible classes: (1) trustworthy; (2) untrustworthy. This study provides a solution to this ongoing debate regarding the topic of trust and its importance. d2c66b5586
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