Abstract: Methods, systems, and devices for processing and answering a natural language query at a database server are described. An end user may submit a question in natural language over a communication platform. An answer engine running on the database server may receive the question, and may process the content of the question using natural language processing (NLP) techniques. The answer engine may construct a search query based on the NLP, and may retrieve a set of documents from a database using the search query. The answer engine may rank the documents, prune the number of documents, modify the documents for the given communication platform, or perform any combination of these functions. In some cases, an intermediate user may review the documents, and may select one or more documents for publication. The answer engine may send the selected documents to the end user as answers in response to the question.

Abstract: Disclosed embodiments are related to feature hashing techniques. A processing device of a database system may identify a set of machine learning features; generate a first hash map of said set of machine learning features and a second different hash map of said set of machine learning features. The processing device may generate a memory compact model for an online machine learning system using the first and second hash maps, and store the memory compact model in the memory device.

Abstract: Methods, systems, and devices for processing and answering a natural language query at a database server are described. An end user may submit a question in natural language over a communication platform. An answer engine running on the database server may receive the question, and may process the content of the question using natural language processing (NLP) techniques. The answer engine may construct a search query based on the NLP, and may retrieve a set of documents from a database using the search query. The answer engine may rank the documents, prune the number of documents, modify the documents for the given communication platform, or perform any combination of these functions. In some cases, an intermediate user may review the documents, and may select one or more documents for publication. The answer engine may send the selected documents to the end user as answers in response to the question.

Abstract: Disclosed are techniques for identifying data items to display in a feed of an enterprise social networking system. A first subset of data items is identified as associated with an inner circle of data sources with which a user interacts at or exceeding a designated frequency, a second subset of data items is identified as associated with an interest graph associated with the user, and a third subset of data items is identified as associated with a group or an organization. A highlights feed can be displayed with different channels, where a first channel is defined by the first subset of data items, a second channel is defined by the second subset of data items, and a third channel is defined by the third subset of data items.

Abstract: In an example, a processing device of a database system may identify a set of machine learning features; generate a first hash map of said set of machine learning features and a second different hash map of said set of machine learning features. The processing device may generate a memory compact model for an online machine learning system using the first and second hash maps, and store the memory compact model in the memory device.

Abstract: Disclosed are some examples of systems, apparatus, methods and storage media for providing customized recommendations to users. Some implementations more particularly relate to a recommendation platform that enables authorized third parties to create, customize and add new recommendations that are then available to be served to target users or audiences of users. Some implementations further relate to a recommendation platform that enables authorized users to define audiences, scheduling settings, scheduling policies, and rules to customize or influence the provision of associated recommendations to the users. The recommendation platform includes a recommendation engine that serves the recommendations to users based on such defined audiences, scheduling settings, policies or other rules.

Abstract: Methods and systems are provided for enhancing user engagement in a social network computing environment by using affinity indices to track the interaction among posts, topics, users, groups, and experts, and populating displays on user, group, and topic pages using the affinity indices.

Abstract: Disclosed are methods, apparatus, systems, and computer program products for identifying relevant feed items to display in a feed of an enterprise social networking system. A plurality of data items from one or more of a plurality of data sources may be identified as relevant to a user by satisfying one or more relevancy parameters comprising: a data source providing the data item being one of an inner circle of data sources, content of the data item being identifiable using an interest graph associated with the user, the data item comprising a mention of the user, and the data item being designated as relevant to a group or an organization with which the user is associated. A highlights feed can include the identified data items as feed items. The feed items can be arranged according to a determined characteristic of each data item.

Abstract: Various implementations are directed to systems, apparatus, computer-implemented methods and storage media for identifying a target set of users of an enterprise network to which to distribute a communication of enterprise-related information. For example, when a communication system receives a request to distribute a communication, the communication system analyzes the communication to identify a set of enterprise users that are predicted to find the information in the communication relevant, and especially, relevant from the enterprise's perspective. For example, the communication system can include a machine learning system that can construct, update and maintain a machine learning model of induction.