A machine learning approach for result caching in web search engines

Highlights：

• To the best of our knowledge, our work is therst in literature to apply machine learning techniques to the result caching problem in search engines, for both static, dynamic, and state-of-the-art static-dynamic cache organizations.

• We evaluate a large set of features and illustrate that they can be exploited to increase the hit rate of result caches.

• We evaluate various oracle caching strategies to illustrate the potential room for improvement in the result caching problem.

• We show that the proposed machine learning framework can improve the hit rate of result caches, potentially reducing the energy consumption in search engines.