Applying reinforcement learning for web pages ranking algorithms

Ranking web pages for presenting the most relevant web pages to user’s queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent’s parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL Rank, is proposed. In RL Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a