# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 3 Related Work  
Ranking factors and their impact on web page rankings in search engines are topics of a lot of research work. Search engines usually publish only a short list of ranking factors, without disclosing their overall relevance in the ranking formula. Because of that, a lot of studies have focused on identifying relevant ranking factors and understanding how they impact web page rankings. In [21] the authors established a mapping between the ranking factors and the web page usability. They concluded that adjusting web pages to the SEO guidelines will, in fact, increase the web page usability and vice versa. In [22] authors researched keyword density and keyword position in the web page content and their impacts on rankings, proposing optimal keyword frequencies. They also built a prediction system for web page ranking. In the study in [3] authors took rank prediction one step further by employing principles of reverse engineering in analyzing highly positioned websites to extract important ranking factors. Comprehensive research on on-page ranking factors that affect web page visibility in search engine results is presented in [4; 5]. The authors explored the impact of keyword frequencies in the page title and body on search results on various search engines. Their experiment showed that having keywords in the page title affects ranking and that the optimal keyword frequency is three. Pages with higher frequencies in the title exhibited the worst results. The analysis of keywords in the web page body (full text) indicated that pages with higher frequencies of keywords ranked higher. In [5] the authors researched the impact of meta data on page rankings. They selected three factors concerning the meta data: \"meta title\" (text in the HTML \"title\" tag), \"meta subject\" (text in the HTML \"meta subject\" tag), and \"meta description\" (text in the HTML \"meta description\" tag). The results showed that pages containing meta data ranked higher than pages without those data. Various combinations of the meta data on pages were also tested. Pages with all three types of meta data ranked better than pages containing just two or one meta data type. The study in [22] extended the previous experiments into several directions. The authors proved that based on only a few factors it is possible to predict a correct rank of seven out of 10 pages on search engines Google and Bing. The dominant factor in their model was \"PR\" (PageRank), which is an off-page factor, the second most important factor was the variable \"HOST\" (domain name), followed by \"TITLE\" (title tag in HTML), \"M_DES\" (meta-description of the HTML document), and \"PATH\" (URL path with the file name of the document). In the same study the authors investigated the SEO experts' scoring. In a survey about ranking factors that was conducted among 37 experts, the following factors were obtained (sorted by importance, from the more important toward the less important ones): keywords in the \"title\" tag, anchor text in inbound links, global popularity of the web page URL, web page age, popularity of internal links, topic relevance of incoming links, popularity of links inside topic groups, keyword in the web page body, global popularity of linked pages, and topic connections of linked pages. In [3] the authors applied reverse engineering to extract the five most important ranking factors: URL length, keyword in the domain name, keyword frequency in H1 tag, keyword frequency in the page title (\"title\" tag), and number of layers in the URL. Similar results about ranking factor importance were obtained in [23; 24; 25; 26], which showed that most of the ranking factors had not considerably changed over the last few years. Author of [27] suggested a metric that combines ranking factors and assesses the page according to the degree of adjustment to the SEO guidelines. The proposed metric is a linear combination of ranking factors that are empirically set (i.e., they are not learned).