Ranking Factors for Web Search : Case Study In The Netherlands
by Tesfay Aregay
It is essential for search engines to constantly adjust ranking function to satisfy their users, at the same time SEO companies and SEO specialists are observed trying to keep track of the factors prioritized by these ranking functions. In this thesis, the problem of identifying highly influential ranking factors for better ranking on search engines is examined in detail, looking at two different approaches currently in use and their limitations. The first approach is to calculate correlation coefficient (e.g. Spearman rank) between a factor and the rank of it's corresponding webpages (ranked document in general) on a particular search engine. The second approach is to train a ranking model using machine learning techniques, on datasets and select the features that contributed most for a better performing ranker. We present results that show whether or not combining the two approaches of feature selection can lead to a significantly better set of factors that improve the rank of webpages on search engines. We also provide results that show calculating correlation coefficients between values of ranking factors and a webpage's rank gives stronger result if a dataset that contains a combination of top few and least few ranked pages is used. In addition list of ranking factors that have higher contribution to well-ranking webpages, for the Dutch web dataset (our case study) and LETOR dataset are provided.
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