This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

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Abstract-In this paper we explore the use of semantic web technologies to mark up data in web pages in the most popular web shops today. Semantic annotations i.e. insertion of structural data within existing HTML document is done for the purpose of enabling aggregating data from various sources that may use different web applications, portals and web search engines. While there are a few studies about the global use of these technologies, there is no any existing detailed research in the field of e-commerce. Also the emergence of new standards (Microdata) and ontologies (GoodRelations and Schema.org) in the field of e-commerce requires more detailed insight into the actual use of the above mentioned. Results of this study show the current situation in this field, and can serve to web shops as a guide for the use of semantic annotation but also to scientists for further research.

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