24. One or more machine-readable tangible and non-transitory media having information for ranking a search result, wherein the information, when read by at least one machine, causes the at least one machine 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 the recency features; and determine when recency is to be utilized for ranking a search result based on the ranking model.
24. One or more machine-readable tangible and non-transitory media having information for ranking a search result, wherein the information, when read by at least one machine, causes the at least one machine 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 the recency features; and determine when recency is to be utilized for ranking a search result based on the ranking model. 29. The media of claim 24 , wherein to train the first ranking model via machine learning comprises train the first ranking model using only the recency features.