1. A system that facilitates outputting indications of usefulness of query suggestions, the system comprising: a processor; and a memory that comprises a plurality of components that are executable by the processor, the plurality of components comprising: a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query; a model component that outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a logistic regression model that has been trained to model user interaction with query suggestions output by a search engine, wherein the logistic regression model has been trained based at least in part upon queries provided to the search engine by users, search results returned to the users subsequent to the search engine performing searches using the queries, search results selected by the users subsequent to the search results being returned to the users, query suggestions provided to the users responsive to the queries being provided to the search engine by the users, query suggestions selected by the users upon the users being provided with the query suggestions, and search results selected by the users subsequent to the users selecting the query suggestions; and a grouping component that groups query suggestions into a plurality of different groups, wherein the model component individually assigns an indication of usefulness to each of the groups of query suggestions, wherein the groups are assembled based at least in part upon an amount of overlap between search results that correspond to query suggestions.
1. A system that facilitates outputting indications of usefulness of query suggestions, the system comprising: a processor; and a memory that comprises a plurality of components that are executable by the processor, the plurality of components comprising: a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query; a model component that outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a logistic regression model that has been trained to model user interaction with query suggestions output by a search engine, wherein the logistic regression model has been trained based at least in part upon queries provided to the search engine by users, search results returned to the users subsequent to the search engine performing searches using the queries, search results selected by the users subsequent to the search results being returned to the users, query suggestions provided to the users responsive to the queries being provided to the search engine by the users, query suggestions selected by the users upon the users being provided with the query suggestions, and search results selected by the users subsequent to the users selecting the query suggestions; and a grouping component that groups query suggestions into a plurality of different groups, wherein the model component individually assigns an indication of usefulness to each of the groups of query suggestions, wherein the groups are assembled based at least in part upon an amount of overlap between search results that correspond to query suggestions. 7. The system of claim 1 , wherein the query suggestion generator component generates a plurality of query suggestions based at least in part upon the received query, and wherein the plurality of components further comprises a reinforcement learning component that analyzes the model component to determine where additional data can be used to train the model component and performs a cost/benefit analysis with respect to candidate query suggestions, the reinforcement learning component selects a query suggestion to provide to an issuer of the query to accumulate additional data that is used to refine the model component.