13. A system for ranking a search result, comprising: a memory comprising instructions executable by one or more processors; and one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to: access a set of recency ranking data comprising one or more past search queries, one or more past search results, and one or more recency features, wherein the recency features comprise: a time-sensitive feature representing a particular time period that was determined based on content of at least some of the past search queries, wherein the at least some of the past search queries were sensitive to the particular time period, and a query timestamp feature representing the time at which the past search queries were received at a search engine; train a first ranking model via machine learning based on recency features; and determine when recency is to be utilized for ranking a search result based on the ranking model.
19. The system of claim 13 , wherein to train the first ranking model via machine learning comprises train the first ranking model using the recency features and a plurality of relevance features.