Abstract: After a host client establishes a multimedia stream with a guest client, host data is received from a host application. A state machine is updated using the host data. The host application executes on the host client. Guest data is received from a guest application. The state machine is updated using the guest data. The guest application executes on the guest client. Transaction data is propagated between the host application and the guest application. The transaction data is presented with the multimedia stream. The transaction data includes the host data and the guest data. Provider data is generated responsive to updating the state machine with the host data and the guest data. The provider data is sent to the guest client. The provider data is presented with the multimedia stream by the guest application on the guest client.

Abstract: Certain aspects of the present disclosure provide techniques for determining query intent and complexity based on text input. One example method generally includes receiving, from a user device, a text query and preprocessing the text query to generate a query vector. The method further includes providing the query vector to an intent model configured to output a user intent of the text query and providing the query vector to a complexity model configured to output a complexity of the text query. The method further includes receiving the user intent of the text query from the intent model and receiving the complexity of the text query from the complexity model. The method further includes determining, based on the user intent and the complexity of the query, a routing target for the text query and routing the text query to the routing target.

Abstract: Method and apparatus for providing personalized self-help experience in online application. A predictive model is trained to learn a relationship between one or more user features and one or more tags using historical user feature data. High-dimensional vectors representing each of a plurality of questions are generated and stored in the lookup table. The trained predictive model outputs tags probabilities from the incoming user data, using the learned relationship. A user high-dimensional vector is formed based on the tags probabilities. Similarity metrics are calculated between the high-dimensional vector for the respective question and the user high dimensional vector. One or more of the most relevant question titles are returned to a client device for presentation to a user.

Abstract: Method and apparatus for providing personalized self-help experience in online application. A predictive model is trained to learn a relationship between one or more user features and one or more tags using historical user feature data. High-dimensional vectors representing each of a plurality of questions are generated and stored in the lookup table. The trained predictive model outputs tags probabilities from the incoming user data, using the learned relationship. A user high-dimensional vector is formed based on the tags probabilities. Similarity metrics are calculated between the high-dimensional vector for the respective question and the user high dimensional vector. One or more of the most relevant question titles are returned to a client device for presentation to a user.