Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for synthetic data generation for service recommendation models. One of the methods includes generating a plurality of synthetic data items, each synthetic data item including user data, context data, service data of a service, and user action data characterizing one or more user actions with respect to the service; for each synthetic data item of the plurality of synthetic data items, processing the synthetic data item using an evaluator to generate a score indicating quality of the synthetic data item; selecting a subset of synthetic data items from the plurality of synthetic data items; and providing the subset of synthetic data items into a service recommendation model for recommending one or more services to one or more users based on the subset of synthetic data items.

Abstract: An enterprise knowledge retention and access system is disclosed. In various embodiments, data comprising a plurality of content items associated specifically with a user is stored. Generative artificial intelligence techniques are used to generate, based at least in part on the plurality of content items associated specifically with the user, a generated content reflecting information derived from the plurality of content items with respect to a specific subject.

Abstract: Object similarity identification and ranking includes identifying multi-feature objects similar to a target object. The identifying is based on a user-specified similarity metric between the target object and each of the multi-feature objects having a value that exceeds a user-specified threshold. One or more weighted performance metrics is determined by weighting each of one or more performance metrics based on a customization input of a user. A gain score is generated for each of the multi-feature objects, each of the gain scores based on the one or more weighted performance metrics and a base score. A recommendation is output, the recommendation based on ranking the multi-feature objects according to the gain score of each multi-feature.

Abstract: Provided are a video call method and apparatus, an electronic device and a storage medium. The method includes: displaying a video call page; in response to receiving a first trigger operation using a first target special effect, determining a first action object of the first target special effect; in response to determining that the first action object includes a target peer user, sending a special effect request to a server to request an acquisition of a first special effect image where the first target special effect is added, where the first special effect image is used for being displayed at a client of at least one user; and in response to receiving the first special effect image forwarded by the server, updating a first video call image displayed in a first display region corresponding to the target peer user as the first special effect image.

Abstract: The present invention relates to an automatic testing apparatus, which comprises a device under test and a testing module. The device under test has a testing program and includes a plurality of functional modules. The testing module is coupled to the device under test. The device under test executes a testing program and communicates with the testing module so that the testing module can test the plurality of functional modules of the device under test. By adopting automatic testing, no tester is needed for performing testing. Thereby, the personnel cost can be reduced and the total testing time can be shortened.

Abstract: The present invention relates to an automatic testing apparatus, which comprises a device under test and a testing module. The device under test has a testing program and includes a plurality of functional modules. The testing module is coupled to the device under test. The device under test executes a testing program and communicates with the testing module so that the testing module can test the plurality of functional modules of the device under test. By adopting automatic testing, no tester is needed for performing testing. Thereby, the personnel cost can be reduced and the total testing time can be shortened.