Abstract: An adaptive multi-model item selection method, comprising: receiving, from one of a plurality of client devices, a request including a client-side feature vector representing a state of the client device; determining, by an advocate model, a probability distribution of a plurality of specialist cluster models from the client-side feature vector; choosing, by a use case selector, a cluster corresponding to a use case from the probability distribution; and obtaining, by the use case selector based on the cluster (i.e., the cluster that was sampled by the user case selector), a specialist cluster model from the plurality of specialist cluster models.

Abstract: An adaptive multi-model item selection method, comprising: receiving, from one of a plurality of client devices, a request including a client-side feature vector representing a state of the client device; determining, by an advocate model, a probability distribution of a plurality of specialist cluster models from the client-side feature vector; choosing, by a use case selector, a cluster corresponding to a use case from the probability distribution; and obtaining, by the use case selector based on the cluster (i.e., the cluster that was sampled by the user case selector), a specialist cluster model from the plurality of specialist cluster models.

Abstract: An adaptive multi-model item selection method, comprising: receiving, from one of a plurality of client devices, a request including a client-side feature vector representing a state of the client device; determining, by an advocate model, a probability distribution of a plurality of specialist cluster models from the client-side feature vector; choosing, by a use case selector, a cluster corresponding to a use case from the probability distribution; and obtaining, by the use case selector based on the cluster (i.e., the cluster that was sampled by the user case selector), a specialist cluster model from the plurality of specialist cluster models.

Abstract: The claimed subject matter provides a system that trains or evaluates ranking techniques by employing or obtaining relative preference judgments. The system can include mechanisms that retrieve a set of documents from a storage device, combine the set of documents with a query or judgment task received via an interface to form a comparative selection panel, and present the comparative selection panel for evaluation by an assessor. The system further requests the assessor to make a selection as to which document included in the set of documents and presented in the comparative selection panel most satisfies the query or judgment task, and thereafter produces a comparative assessment of the set of documents based on the selections elicited from the assessor and associated with the set of documents.

Abstract: The claimed subject matter provides a system that trains or evaluates ranking techniques by employing or obtaining relative preference judgments. The system can include mechanisms that retrieve a set of documents from a storage device, combine the set of documents with a query orjudgment task received via an interface to form a comparative selection panel, and present the comparative selection panel for evaluation by an assessor. The system further requests the assessor to make a selection as to which document included in the set of documents and presented in the comparative selection panel most satisfies the query or judgment task, and thereafter produces a comparative assessment of the set of documents based on the selections elicited from the assessor and associated with the set of documents.