Abstract: Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.

Abstract: A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.

Abstract: Various embodiments include systems and methods for data mining of search engine and network operations to automatically identify query events. Data aggregated from such query events and stored as query history data may be processed to identify query ranking mismatches. These identified mismatches may be used with the query history data and target settings to automatically generate query rewrite data. In certain embodiments, this query rewrite data may be used to generate query rewrite input language (QRIL) records. Such QRIL records may then be used to automatically generate standardized rewrites which automatically resolve any conflicts between rewrites in a particular search engine.

Abstract: A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.

Abstract: Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: Various embodiments include systems and methods tier processing query rewrite records to generate standardized query rewrites usable by a search engine. Such systems and method may involve analysis of query rewrite input language (QRIL) records to identify relationships and conflicts between multiple QRIL records, and to resolve these relationships and conflicts to generate a standardized rewrite in a semantic language recognizable by the search engine. Such systems and methods may gather QRIL records from a QRIL record database, process the QRIL records using precedence rules, and then communicate a set of standardized and optimized query rewrites to the search engine.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted—with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: Various embodiments include systems and methods for generating query rewrite records which may be used to generate standardized query rewrites for a search engine. Such records may identify rewrite triggers as well as constraints and other metadata flags which may be associated with certain rewrites in query rewrite identification (QRIL) records. In certain embodiments, such records may he analyzed with other QRIL records or rewrite information to prevent rewrite conflicts and to generate standardized rewrites. This information may then be used by a search engine to generate responses to user queries.

Abstract: Various embodiments include systems and methods for generating query rewrite records which may be used to generate standardized query rewrites for a search engine. Such records may identify rewrite triggers as well as constraints and other metadata flags which may be associated with certain rewrites in query rewrite identification (QRIL) records. In certain embodiments, such records may be analyzed with other QRIL records or rewrite information to prevent rewrite conflicts and to generate standardized rewrites. This information may then be used by a search engine to generate responses to user queries.

Abstract: Various embodiments include systems and methods for data mining of search engine and network operations to automatically identify query events. Data aggregated from such query events and stored as query history data may be processed to identify query ranking mismatches. These identified mismatches may be used with the query history data and target settings to automatically generate query rewrite data. In certain embodiments, this query rewrite data may be used to generate query rewrite input language (QRIL) records. Such QRIL records may then be used to automatically generate standardized rewrites which automatically resolve any conflicts between rewrites in a particular search engine.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: Various embodiments include systems and methods for data mining of search engine and network operations to automatically identify query events. Data aggregated from such query events and stored as query history data may be processed to identify query ranking mismatches. These identified mismatches may be used with the query history data and target settings to automatically generate query rewrite data. In certain embodiments, this query rewrite data may be used to generate query rewrite input language (QRIL) records. Such QRIL records may then be used to automatically generate standardized rewrites which automatically resolve any conflicts between rewrites in a particular search engine.

Abstract: Various embodiments include systems and methods for data mining of search engine and network operations to automatically identify query events. Data aggregated from such query events and stored as query history data may be processed to identify query ranking mismatches. These identified mismatches may be used with the query history data and target settings to automatically generate query rewrite data. In certain embodiments, this query rewrite data may be used to generate query rewrite input language (QRIL) records. Such QRIL records may then be used to automatically generate standardized rewrites which automatically resolve any conflicts between rewrites in a particular search engine.

Abstract: Various embodiments include systems and methods for generating query rewrite records which may be used to generate standardized query rewrites for a search engine. Such records may identify rewrite triggers as well as constraints and other metadata flags which may be associated with certain rewrites in query rewrite identification (QRIL) records. In certain embodiments, such records may be analyzed with other QRIL records or rewrite information to prevent rewrite conflicts and to generate standardized rewrites. This information may then be used by a search engine to generate responses to user queries.

Abstract: Various embodiments include systems and methods tier processing query rewrite records to generate standardized query rewrites usable by a search engine. Such systems and method may involve analysis of query rewrite input language (QRIL) records to identify relationships and conflicts between multiple QRIL records, and to resolve these relationships and conflicts to generate a standardized rewrite in a semantic language recognizable by the search engine. Such systems and methods may gather QRIL records from a QRIL record database, process the QRIL records using precedence rules, and then communicate a set of standardized and optimized query rewrites to the search engine.

Abstract: A user may submit a search query to a search engine. The search engine may process the search query and generate a set of results. Each of the items searched by the search engine may have been pre-assigned to a category in a category tree. Previous interactions by other users with the items after similar queries may have been recorded. The search engine may identify categories based on the distribution of the interacted-with results among the categories. The category tree may be analyzed at different levels, based on the entropy observed at each level. A level with low entropy may be chosen, and categories at that level used to constrain the query.

Abstract: A user may select text, an image, or other content being displayed on a device. The device may generate a search query based on the selection and transmit the search query to a server. Based on the search query, the server may generate multiple search options. The server may rank the generated search options and transmit some or all of the generated search options to the user device. The user device may receive the transmitted search options and present one or more of them in a user interface. The user may activate a search option. The user device may transmit the activated search option to the server, which may respond with search results for the activated search option. The user device may then display the search results to the user.

Abstract: A user may submit a search string to a system. Before processing the search, the system may analyze the search string and modify it. For example, the search string may be modified by treating some terms as a phrase, by dropping some terms, by treating some terms as attributes, or any suitable combination thereof. The modification of the search string may be based on an analysis of prior search strings and user actions. The results of a search based on the modified search string may be returned to the user.