Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

Abstract: In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q1, . . . , qn}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, qj, in the query chain, where 2?j?n, if one of the network resources identified for qj has actually been clicked in connection with qj by the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, qk, in the query chain, where 1?k<j.

Abstract: In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

Abstract: One embodiment accesses a search query and one or more sets of clicked network resources corresponding to the search query; determines a classifier model that represents the sets of clicked network resources that each satisfy the information need of one of the users and one or more subsets of the sets of clicked network resources that each do not satisfy the information need of one of the users; computes a probability value for each clicked network resource from each of the sets of clicked network resources using the classier model, wherein the probability value represents a likelihood that, after clicking on the corresponding network resource, the particular one of the users conducting the corresponding particular one of the search sessions ends the search session; and forms a set of features comprising the probability values computed for network resources from the search sessions.

Abstract: In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.