Abstract: Systems and methods of automated machine learning for modeling a data set according to a modeling intent are presented. A modeling service receives a data set from a submitting party as well as a set of constraints. A pipeline generator generates a set of pipelines according to a modeling intent of a data set and in view of the set of constraints. A machine learned trained judge conducts an analysis of the pipelines to identify an optimal pipeline to train. Optimal results are generated according to the optimal pipeline and the optimal results are provided to the submitting party in response to receiving the data set and constraints.

Abstract: Systems and methods of generating recipes for modeling data sets are presented. Based on a corpus of a plurality of recipes for modeling data sets, an analysis is made. The analysis is conducted on the plurality of recipes in light of a corresponding plurality of modeling metrics, resulting in the identification of performance bases of the processing steps of the plurality of recipes. A determination of a new recipe, not already included in the corpus of recipes, is made. The determination is made according to the identifies bases, the new recipe comprising a plurality of processing steps for processing the data set. A pipeline generated for the new recipe is obtained and submitted to a trained judge for evaluation. Predicted results for the new recipe is obtained from the trained judge and the recipe and predicted results are stored in the corpus of recipes.

Abstract: Systems and methods of automated machine learning for modeling a data set according to a modeling intent are presented. A modeling service receives a data set from a submitting party as well as a set of constraints. A pipeline generator generates a set of pipelines according to a modeling intent of a data set and in view of the set of constraints. A machine learned trained judge conducts an analysis of the pipelines to identify an optimal pipeline to train. Optimal results are generated according to the optimal pipeline and the optimal results are provided to the submitting party in response to receiving the data set and constraints.

Abstract: Systems and methods for evaluating the evaluation behaviors of an evaluator are presented. In contrast to evaluation methods that monitor and analyze click behaviors, the disclosed subject matter is directed to evaluating non-click behaviors. After obtaining results of an evaluation request submitted to a response service for evaluation by the evaluator, evaluation behaviors of the evaluator are monitored. The monitored evaluation behaviors are in association with an evaluation of the obtained results and one or more heuristics or rules are applied to the monitored evaluation behaviors to determining whether the monitored evaluation behaviors are within predetermined quality thresholds. If the monitored evaluation behaviors are not within the predetermined quality thresholds, the monitored evaluation behaviors are flagged as anomalous evaluation behaviors.

Abstract: Simplified collaborative searching is provided by pattern recognition such as facial recognition, motion recognition, and the like to provide handsfree functionality. Users join a collaborative search by placing themselves within the field of view of a camera communicationally coupled to a computing device that performs facial recognition and identifies the users, thereby adding them to the collaboration. Users also join by performing simple movements with a portable computing device, such as the ubiquitous mobile phone. A collaboration component tracks the users in the collaboration and identifies them to a search engine, thereby enabling the search engine to perform a collaborative search. The collaboration component also disseminates the collaborative recommendations, either automatically or based upon explicit requests triggered by pattern recognition, including motion recognition and touch recognition.

Abstract: Simplified collaborative searching is provided by pattern recognition such as facial recognition, motion recognition, and the like to provide handsfree functionality. Users join a collaborative search by placing themselves within the field of view of a camera communicationally coupled to a computing device that performs facial recognition and identifies the users, thereby adding them to the collaboration. Users also join by performing simple movements with a portable computing device, such as the ubiquitous mobile phone. A collaboration component tracks the users in the collaboration and identifies them to a search engine, thereby enabling the search engine to perform a collaborative search. The collaboration component also disseminates the collaborative recommendations, either automatically or based upon explicit requests triggered by pattern recognition, including motion recognition and touch recognition.

Abstract: The disclosed architecture enables user feedback in the form of gestures, and optionally, voice signals, of one or more users, to interact with a search engine framework. For example, document relevance, document ranking, and output of the search engine can be modified based on the capture and interpretation of physical gestures of a user. The recognition of a specific gesture is detected based on the physical location and movement of the joints of a user. The architecture captures emotive responses while navigating the voice-driven and gesture-driven interface, and indicates that appropriate feedback has been captured. The feedback can be used to alter the search query, personalize the response using the feedback collected through the search/browsing session, modifying result ranking, navigation of the user interface, modification of the entire result page, etc., among many others.