Listwise vs Pagewise: Towards Better Ranking Strategies for Heterogeneous Search Results

Abstract

As heterogeneous verticals account for more and more in search engines, users' preference of search results is largely affected by their presentations. Apart from texts, multimedia information such as images and videos has been widely adopted as it makes the search engine result pages (SERPs) more informative and attractive. It is more proper to regard the SERP as an information union, not separate search results because they interact with each other. Considering these changes in search engines, we plan to better exploit the contents of search results displayed on SERPs through deep neural networks and formulate the pagewise optimization of SERPs as a reinforcement learning problem.