A novel web ranking algorithm based on pages multi-attribute

The size of the Internet is rapidly increasing. It has become a necessity to access information on the web correctly for a short time. For this reason, search engines have arisen out of meeting this need. In this study, we propose a ranking algorithm based on page multi-attribute (PMARank). The proposed algorithm uses a novel index calculation system that acts as a pre-rank process for web pages. In the ranking procedure, the featured meta-tag of a page and its contents were extracted to locate words as ranking features. The proposed web ranking algorithm has been compared with PageRank (PR) and Hyperlink-Induced Topic Search (HITS) algorithms. Experimental results show that the proposed ranking algorithm performs better than PR and HITS algorithms according to user clickstreams on the search results page. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.