Abstract: Methods, systems, and media for presenting user comments containing timed references in synchronization with a media content item are provided.

Abstract: Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Abstract: Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Abstract: Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Abstract: Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Abstract: Computer-readable media and computerized methods are provided for identifying terms associated with one or more queries, including aspects directed to manipulating a list of terms associated with a query class by extracting terms, identifying equivalences or relevant terms, and condensing or expanding the list of terms. In embodiments, synonymous terms can be identified. An updated list of terms can be written to a storage location in association with a query or query class.

Abstract: Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Abstract: Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Abstract: Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Abstract: The disclosed architecture enables user feedback in the form of gestures, and optionally, voice signals, of one or more users, to interact with a search engine framework. For example, document relevance, document ranking, and output of the search engine can be modified based on the capture and interpretation of physical gestures of a user. The recognition of a specific gesture is detected based on the physical location and movement of the joints of a user. The architecture captures emotive responses while navigating the voice-driven and gesture-driven interface, and indicates that appropriate feedback has been captured. The feedback can be used to alter the search query, personalize the response using the feedback collected through the search/browsing session, modifying result ranking, navigation of the user interface, modification of the entire result page, etc., among many others.

Abstract: Computer-readable media and computerized methods are provided for identifying terms associated with one or more queries, including aspects directed to manipulating a list of terms associated with a query class by extracting terms, identifying equivalences or relevant terms, and condensing or expanding the list of terms. In embodiments, synonymous terms can be identified. An updated list of terms can be written to a storage location in association with a query or query class.

Abstract: Various technologies described herein pertain to using social activity data to personalize ranking of results returned by a computing operation for a user. For each of the results returned by the computing operation, a respective first affinity of the user to a corresponding result and a respective second affinity of the user to the corresponding result can be calculated and used for ranking the results. The respective first affinity of the user to the corresponding result can be calculated based on correlations between social activity data of the user and social activity data of a first group of historical users that clicked the corresponding result. Moreover, the respective second affinity of the user to the corresponding result can be calculated based on correlations between the social activity data of the user and social activity data of a second group of historical users that skipped the corresponding results.

Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Abstract: User locality information can be used to improve various aspects of search results pages. Queries can be suggested based on the user location while excluding common query suggestions that involve an unrelated geographic entity. Deeplinks can also be modified to include location based suggestions. Additionally, results for specialized searches such as travel searches can be improved by employing user locality information.

Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Abstract: Methods, systems, and computer-readable media are provided for updating a search index with new uniform resource locators (URLs) and spiking URLs with increased user interest. History data, provided from browser applications residing on users' computers that indicate URLs accessed by the users, is parsed to identify new/previously unknown URLs. The history data also indicates URLs in which there is increased interest based on a number of recent hits as compared to an average number of hits determined over time. Author postings of new URLs to social networking sites and a quality rating of the authors may also be used to identify and filter new URLs. Search indexes are updated with the new and spiking URL data. As such, lag time between posting of new URLs and spiking of URL interest and inclusion of this data in a search index is greatly decreased.

Abstract: Computer systems, methods, and media improving the relevance of the search results presented to a user for queries having an implicit local intent are provided. Utilizing the systems and methods described herein locally-biased queries are identified, despite such queries being void of any location-specific terms. A location of a computing device associated with the user is determined and localized web search results associated with the data item pair comprising the input query and the user's determined location are generated and presented to the user.

Abstract: Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Abstract: Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Abstract: User locality information can be used to improve various aspects of search results pages. Queries can be suggested based on the user location while excluding common query suggestions that involve an unrelated geographic entity. Deeplinks can also be modified to include location based suggestions. Additionally, results for specialized searches such as travel searches can be improved by employing user locality information.