Every step in the evolution of human kind is associated with the inherent quest for knowledge and substantial growth in intelligence. In the modern world, the thirst for information is quenched by search engines that crawl billions of pages on the World Wide Web. This paper endeavors to make the ranking of the indexed web pages more intelligent by using techniques followed by recommendation engines that, with the help of some algorithms, recommend products on e-commerce websites. The focus primarily lies on discovering user groups, finding the degree of similarity between users based on search queries and building a graph that tracks the clicks on search results within the group, enabling the machine to learn which result might meet the expectation of one particular user and rank the results accordingly.

Search engine ranking algorithms intelligent ranking recommendation systems.