In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

A content management system may instantiate, from the same super class defined in a database schema, principal objects representing users and groups and content objects representing documents and folders. The principal objects and the content objects share the same social interaction functions. When a content object is modified, the system can create a message in which the content object identifies itself as a first person, update a message table such that any follower of the content object is notified of the message, and update a profile or feed associated with the content object. At least because content objects can “socialize” like principal objects, the system can generate a social graph containing content objects as nodes, map relationships among principal objects and content objects, and make recommendations to perhaps change/enhance such relationships.

A content management system may instantiate, from the same super class defined in a database schema, principal objects representing users and groups and content objects representing documents and folders. The principal objects and the content objects share the same social interaction functions. When a content object is modified, the system can create a message in which the content object identifies itself as a first person, update a message table such that any follower of the content object is notified of the message, and update a profile or feed associated with the content object. At least because content objects can “socialize” like principal objects, the system can generate a social graph containing content objects as nodes, map relationships among principal objects and content objects, and make recommendations to perhaps change/enhance such relationships.

Technology is described herein for ranking candidate result items in at least two stages. In a first stage, the technology uses a first attention-based neural network to determine an extent of attention that each token of an input query should pay to the tokens of each candidate result item. In a second stage, the technology uses a ranking subsystem to perform listwise inference on output results provided by the first stage, to generate a plurality of ranking scores that establish an order of relevance of the candidate results items. The ranking subsystem may use a second attention-based neural network to perform the listwise inference. According to some implementations, the technology is configured to process queries and candidate result items having different kinds and combinations of features. For instance, one kind of input query may include text-based features, structure-based features, and geographic-based features.

Technology is described herein for ranking candidate result items in at least two stages. In a first stage, the technology uses a first attention-based neural network to determine an extent of attention that each token of an input query should pay to the tokens of each candidate result item. In a second stage, the technology uses a ranking subsystem to perform listwise inference on output results provided by the first stage, to generate a plurality of ranking scores that establish an order of relevance of the candidate results items. The ranking subsystem may use a second attention-based neural network to perform the listwise inference. According to some implementations, the technology is configured to process queries and candidate result items having different kinds and combinations of features. For instance, one kind of input query may include text-based features, structure-based features, and geographic-based features.

In an approach to interpreting and summarizing online terms of use, a computer receives a terms of use agreement from a data source and a request for interpretation of the terms of use agreement from a user. A computer categorizes the terms of use agreement into one or more categories based on a type of data source of the terms of use agreement. A computer ranks one or more words in the terms of use agreement based on the categorization and on one or more additional terms of use agreements of the type of data source. A computer generates a summary of the terms of use agreement based on the ranking. A computer displays the summary of the terms of use agreement to the user. A computer receives input from the user associated with consent to the terms of use agreement. A computer stores the input.

In an approach to interpreting and summarizing online terms of use, a computer receives a terms of use agreement from a data source and a request for interpretation of the terms of use agreement from a user. A computer categorizes the terms of use agreement into one or more categories based on a type of data source of the terms of use agreement. A computer ranks one or more words in the terms of use agreement based on the categorization and on one or more additional terms of use agreements of the type of data source. A computer generates a summary of the terms of use agreement based on the ranking. A computer displays the summary of the terms of use agreement to the user. A computer receives input from the user associated with consent to the terms of use agreement. A computer stores the input.

Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface (GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.

Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface (GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.

Systems, methods, and computer-program products are described for determining current location information which includes determining whether an object is moving by comparing the current location information with previous location information. Future location information is calculated and a point of interest is filtered using the future location information. A media asset is then matched to the filtered point of interest and a current score is determined for the matched media asset. Determining a current score includes determining a window in which the matched media asset is geographically relevant to the future location information, and updating the matched media asset score when the future location information changes. An ordered playlist is dynamically generated according to the future location information by repeatedly prioritizing and sequencing matched media assets according to the current score for each matched media asset, and matched media assets are played or displayed in the ordered playlist.