Abstract: Traditional search engines, which only return sorted relevant search results based on keyword queries, can no longer meet users’ increasingly diverse information needs. To address this, search result diversification ranking technology has emerged. On the basis of maintaining query relevance, this technology considers the novelty of different documents and users’ potential diverse query intents, presenting users with ranked results that are more comprehensive and rich in content. With the rapid development and breakthroughs of deep learning technology, deep learning models such as generative adversarial networks(GANs) and graph neural networks(GNNs) have also been widely applied in the field of search result diversification ranking. Although a large number of latest research achievements in search result diversification ranking have emerged recently, there is a lack of review work on newly proposed search result diversification ranking methods and other related studies. Based on this, this paper conducts a relatively comprehensive review of the research progress in search result diversification ranking over the past 5 years. Firstly, it reviews the development status of search result diversification ranking and related reviews, and expounds on the definition of the search result diversification ranking problem. Secondly, it classifies the latest methods of search result diversification ranking in the past 5 years, and focuses on analyzing the representative methods in each category. Thirdly, it elaborates on and analyzes mainstream and novel evaluation metrics for search result diversification, summarizes the existing major datasets for search result diversification, sorts out and analyzes the performance of the latest search result diversification ranking methods, and finally summarizes the application progress of current search result diversification ranking technologies. Furthermore, it looks forward to the future research directions of search result diversification ranking, aiming to provide references for relevant researchers in their studies on search result diversification ranking and promote further development and innovation in this field.

Key words: information retrieval, search result diversification, diversified ranking, learning to rank, deep learning ,