Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: User information for a particular user is accessed. Expert information for experts and training that is available in an organization of the particular user is accessed. One or more pattern matches between the user information and the expert information are determined. One or more expert recommendations are generated based on the one or more pattern matches and provided.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Methods, systems, and computer-readable storage media for receiving, from an integrated development environment (IDE), authored code, the authored code having been input by a developer to the IDE, receiving developer data representative of the developer, and context data and domain data associated with the authored code, determining, at least partially based on processing the authored code, the developer data, the context data, and the domain data through one or more machine learning (ML) models, that one or more code recommendations are to be displayed to the developer in the IDS, defining a sub-set of code recommendations at least partially based on the authored code and the context data, the sub-set of code recommendations comprising at least one code snippet, and displaying graphical representations of code recommendations in the sub-set of code recommendations to the developer within the IDE.

Abstract: Input data is received, by a server, for a user from one or more data sources. A set of user language features associated with the input data are determined. The set of user language features is compared to multiple sets of known language features. Each set of known language features includes language features associated with the use of a non-native language by a speaker of a respective native language. A native language of the user is determined based on the comparing. A personalized recommendation is generated based on the determined native language of the user.

Abstract: Methods, systems, and computer-readable storage media for receiving, from an integrated development environment (IDE), authored code, the authored code having been input by a developer to the IDE, receiving developer data representative of the developer, and context data and domain data associated with the authored code, determining, at least partially based on processing the authored code, the developer data, the context data, and the domain data through one or more machine learning (ML) models, that one or more code recommendations are to be displayed to the developer in the IDS, defining a sub-set of code recommendations at least partially based on the authored code and the context data, the sub-set of code recommendations comprising at least one code snippet, and displaying graphical representations of code recommendations in the sub-set of code recommendations to the developer within the IDE.

Abstract: User information for a particular user is accessed. Application information for applications that are available in an organization of the particular user is accessed. One or more pattern matches between the user information and the application information are determined. One or more application recommendations are generated based on the determined one or more pattern matches. The one or more application recommendations are provided.

Abstract: First resource utilization information for a first customer of a cloud platform and second resource utilization information for a second customer of the cloud platform are accessed. A first prediction regarding future resource utilization by the first customer and a second prediction regarding future resource utilization by the second customer are determined. A resource reallocation recommendation that recommends reallocating one or more resources between the first customer and the second customer is determined, based on the first prediction and the second prediction. The resource reallocation recommendation is provided.

Abstract: User information for a particular user is accessed. Expert information for experts and training that is available in an organization of the particular user is accessed. One or more pattern matches between the user information and the expert information are determined. One or more expert recommendations are generated based on the one or more pattern matches and provided.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: First resource utilization information for a first customer of a cloud platform and second resource utilization information for a second customer of the cloud platform are accessed. A first prediction regarding future resource utilization by the first customer and a second prediction regarding future resource utilization by the second customer are determined. A resource reallocation recommendation that recommends reallocating one or more resources between the first customer and the second customer is determined, based on the first prediction and the second prediction. The resource reallocation recommendation is provided.

Abstract: User information for a particular user is accessed. Application information for applications that are available in an organization of the particular user is accessed. One or more pattern matches between the user information and the application information are determined. One or more application recommendations are generated based on the determined one or more pattern matches. The one or more application recommendations are provided.

Abstract: User information for a particular user is accessed. Application interface and artifact information for application interfaces and artifacts that are available in an organization of the particular user is accessed. One or more pattern matches are determined between the user information and the application interface and artifact information. One or more interface or artifact recommendations are generated based on the determined one or more pattern matches. The one or more interface or artifact recommendations are provided.

Abstract: Input data is received, by a server, for a user from one or more data sources. A set of user language features associated with the input data are determined. The set of user language features is compared to multiple sets of known language features. Each set of known language features includes language features associated with the use of a non-native language by a speaker of a respective native language. A native language of the user is determined based on the comparing. A personalized recommendation is generated based on the determined native language of the user.

Abstract: A request for recommending an item is received from a clients server, wherein the request comprises baskets data, and the basket data are associated with multiple baskets and multiple items. At least one recommendation model is built based on an Optimized Recommendations based Basket Size (ORBS) algorithm. The baskets data is processed using the recommendation model to generate a recommendation result, and the recommendation result comprises a recommended item. The recommendation result is sent to the client server to render the recommended item to be displayed on a user interface of a user device.

Abstract: A computer-implemented method for using a third-party recommendation in a game system includes: receiving, at a recommendation engine and from a game system, current game context data provided in real time during a game in which at least one user participates; selecting, by the recommendation engine, at least one of multiple predefined actions based on at least the current game context data; and providing a response to the game system that identifies the selected at least one action for performance.

Abstract: A computer-implemented method for using a third-party recommendation in a game system includes: receiving, at a recommendation engine and from a game system, current game context data provided in real time during a game in which at least one user participates; selecting, by the recommendation engine, at least one of multiple predefined actions based on at least the current game context data; and providing a response to the game system that identifies the selected at least one action for performance.

Abstract: According to some embodiments, a central hub may establish a preference profile associated with a user, and the preference profile may include, for example at least some information not provided by the user. Catalogue data may then be exchanged between the central hub and a first and second retailer that store product information in association with a enterprise data management systems. The central hub may receive user information associated with the user (including location information) from a remote user device. Based on (i) the user information, (ii) the catalogue data from the first and second retailers, and (iii) a set of business rules established at the central hub, an offer may be selected and information about the selected offer may be transmitted to the remote user device.