Abstract: Provided are methods, systems, devices, and tangible non-transitory computer readable media for providing data associated with a machine-learned model library. The disclosed technology can perform operations including providing a machine-learned GP model library that includes a plurality of machine-learned models trained to generate semantic observations based on sensor data associated with a vehicle. Each machine-learned model of the plurality of machine-learned models can be associated with one or more configurations supported by each machine-learned model. A request for a machine-learned model from the machine-learned model library can be received a remote computing device. Furthermore, based on the request, the machine-learned model can be provided to the remote computing device.

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

Abstract: Systems and methods for determining correlation scores for product pairs are provided. Contextual user behavior indicator data relating to a plurality of user behavior indicator types is received. A correlation score is computed for a first product and a second product for each user behavior indicator type from the plurality of user behavior indicator types. A final correlation score is computed for the first product and the second product by combining the computed correlation scores for each user behavior indicator type. The computed final correlation score for the first product and the second product is stored into a first data storage.