Research on joint ranking recommendation model based on Markov chain

In this paper, a supervised learning framework with strong expansibility is first established for search engine joint ranking problem. It can transform existing algorithms into corresponding learning algorithms, and design new algorithms under this framework. Second, with Markov chain model as the core algorithm, this paper combines the ranking results of three main factors, including content relevance, hyperlink prediction, and query click behavior, and transforms the joint problem of ranking results into a positive semi-definite programming problem, and deduces the detailed process of solving the problem. Finally, this paper analyzes the rationality and efficiency of the joint ranking recommendation model based on Markov chain by setting the weight coefficient through experimental data.