Abstract: Non-limiting examples of the present disclosure describe a unified ranking model that may be used by a plurality of entry points to return ranked results in response to received query data. The unified ranking model is provided as a service for a plurality of entry points. A query is received from an entry point of the plurality of entry points. Results data for the query data is retrieved. A unified ranking model is executed to rank the results data. Execution of the unified ranking model manipulates feature data of the unified ranking model based on user context signals associated with the received query data and acquired result retrieval signals corresponding with the retrieved results data. Execution of the unified ranking model generates ranked result data. Ranked results data is returned to the processing device corresponding with the entry point. Other examples are also described.

Abstract: Systems and methods for automated generation of new content responses to answer user queries are provided. The systems and methods for automated generation of new content responses answer user queries utilizing deep learning and a reasoning algorithm. The generated response is composed of new content and is not merely cut or copied information from one or more search results. Accordingly, the systems and methods for automated generation of new content responses provide tailored query specific answers that can be long and detailed including several sentences of information or that can be short and concise, such as “yes” or “no.” The ability of the systems and methods described herein to create or generate new content in response to a user query improves the usability, improves the performance, and/or improves user interactions of/with a search query system.

Abstract: Ranking domains for search engines is provided herein. To rank a domain, contributing domains associated with the domain are identified. Additionally, the maturity of each of the contributing domains is determined. A rank for the domain is then determined based at least in part on the maturity of each of the contributing domains. The domain rankings may then be used to order results for search queries.

Abstract: Non-limiting examples of the present disclosure describe a unified ranking model that may be used by a plurality of entry points to return ranked results in response to received query data. The unified ranking model is provided as a service for a plurality of entry points. A query is received from an entry point of the plurality of entry points. Results data for the query data is retrieved. A unified ranking model is executed to rank the results data. Execution of the unified ranking model manipulates feature data of the unified ranking model based on user context signals associated with the received query data and acquired result retrieval signals corresponding with the retrieved results data. Execution of the unified ranking model generates ranked result data. Ranked results data is returned to the processing device corresponding with the entry point. Other examples are also described.

Abstract: Architecture that performs the automatic modeling of user preferences for entities (a personal entity preference model) based on user's actions such as search history and temporal search behavior to determine content on the web relevant and of interest to a given user at any given time. Explicit and implicit user responses (e.g., notification clicks, ignore, dismiss, unsubscribe, notification dwell) are used to update the model of user entity preferences. The user entity preference model is used to order notifications based on predicted relevance. Additionally, the user personal entity preference model and implicit responses of user are used to decide timing and frequency of notifications.

Abstract: Systems, computer-implemented methods, and computer-readable media for modifying the rank of search result items returned by a search engine are provided. A search engine determines a plurality of search result items that satisfy a user query and the order the search result items are to be presented to a user. A rank modifier determines whether any modification should be made to the rank of each search result item identified by the search engine. The rank of search result items identified as potential spam may be demoted while the rank of search result items identified to be in the language of the search query, having a high click-through rate, or as containing adjacent search terms from the search query may be promoted. The search result items are presented according to modified ranking to the querying user.

Abstract: Searching for a subset of the keywords in a search-engine query is described herein. The search-engine query is parsed into keywords. The keywords are checked against an inverted index to determine whether any web documents include the subset of keywords. Documents containing the subset of keywords are listed in a search-results list and transmitted back to the user.

Abstract: The claimed subject matter provides a system and/or a method that facilitates utilizing transition probability in static rankings associated with at least one document. An interface can receive data related to a query, wherein the query can be associated with a search from a user. A rank component can provide query results that are prioritized utilizing a transition probability based on user activity included within a user log.

Abstract: The claimed subject matter provides a system and/or a method that facilitates providing a personalized set of available content. An interface can receive a content vector associated with available content and a profile vector associated with a user preference. Additionally, a personalization component can generate a personalized set of available content by correlating the content vector and the profile vector.

Abstract: The claimed subject matter provides systems and/or methods that enable scalable collaborative filtering. A collaborative filtering component can receive data associated with a plurality of user sessions and data associated with at least one of a user and an item. Additionally, the collaborative filtering component can generate a recommendation based on a similarity. Further, an approximation component can approximate the similarity between at least one of the item and disparate items and the user and disparate users.

Abstract: Various technologies and techniques are disclosed that improve the identification of related content. An article for which to identify matching content is received or selected. The raw text of the article is analyzed to reduce the raw text to a core set of words, and the results are stored in a document feature vector array. The formatted text of the article is analyzed and vector array scores are updated based on the formatting. Anchor text words for documents that link to the article are added to the vector array. Articles linking to and from the particular article are identified and added to the vector array as appropriate. Transformations are performed, such as to adjust the vector scores based on how common or generic the words are. Vector arrays are created for other potentially related documents. The vectors are compared to determine how related they are to each other.

Abstract: Ranking domains for search engines is provided herein. To rank a domain, contributing domains associated with the domain are identified. Additionally, the maturity of each of the contributing domains is determined. A rank for the domain is then determined based at least in part on the maturity of each of the contributing domains. The domain rankings may then be used to order results for search queries.

Abstract: Various technologies and techniques are disclosed that improve the identification of related content. An article for which to identify matching content is received or selected. The raw text of the article is analyzed to reduce the raw text to a core set of words, and the results are stored in a document feature vector array. The formatted text of the article is analyzed and vector array scores are updated based on the formatting. Anchor text words for documents that link to the article are added to the vector array. Articles linking to and from the particular article are identified and added to the vector array as appropriate. Transformations are performed, such as to adjust the vector scores based on how common or generic the words are. Vector arrays are created for other potentially related documents. The vectors are compared to determine how related they are to each other.

Abstract: The claimed subject matter provides systems and/or methods that enable scalable collaborative filtering. A collaborative filtering component can receive data associated with a plurality of user sessions and data associated with at least one of a user and an item. Additionally, the collaborative filtering component can generate a recommendation based on a similarity. Further, an approximation component can approximate the similarity between at least one of the item and disparate items and the user and disparate users.

Abstract: The claimed subject matter provides a system and/or a method that facilitates utilizing transition probability in static rankings associated with at least one document. An interface can receive data related to a query, wherein the query can be associated with a search from a user. A rank component can provide query results that are prioritized utilizing a transition probability based on user activity included within a user log.

Abstract: The claimed subject matter provides a system and/or a method that facilitates providing a personalized set of available content. An interface can receive a content vector associated with available content and a profile vector associated with a user preference. Additionally, a personalization component can generate a personalized set of available content by correlating the content vector and the profile vector.