Abstract: Methods and systems for generating a combined metric parameter for A/B testing comprising: acquiring a respective first metric parameter for a first and second plurality of feature vectors, a combination of the respective first metric parameters being indicative of a direction of a change in user interactions between the control version and the treatment version, acquiring a respective second metric parameter for the first and second plurality of feature vectors, a combination of the respective second metric parameters being indicative of a magnitude of the change in user interactions between the control and treatment version, generating a respective combined control metric parameter for the first plurality of feature vectors and the second plurality of feature vectors, the combination of the respective combined metric parameters being simultaneously indicative of the magnitude and the direction of the change in user interactions between the control and treatment version.

Abstract: Methods and systems for selecting a selected-sub-set of features from a plurality of features for training a machine learning module, the training of the machine learning module to enable classification of an electronic document to a target label, the plurality of features associated with the electronic document. In one embodiment, the method comprises analyzing a given training document to extract the plurality of features, and for a given not-yet-selected feature of the plurality of features: generating a set of relevance parameters iteratively, generating a set of redundancy parameters iteratively and determining a feature significance score based on the set of relevance parameters and the set of redundancy parameters. The method further comprises selecting a feature associated with a highest value of the feature significance score and adding the selected feature to the selected-sub-set of features.

Abstract: Methods and systems for generating a combined metric parameter for A/B testing comprising: acquiring a respective first metric parameter for a first and second plurality of feature vectors, a combination of the respective first metric parameters being indicative of a direction of a change in user interactions between the control version and the treatment version, acquiring a respective second metric parameter for the first and second plurality of feature vectors, a combination of the respective second metric parameters being indicative of a magnitude of the change in user interactions between the control and treatment version, generating a respective combined control metric parameter for the first plurality of feature vectors and the second plurality of feature vectors, the combination of the respective combined metric parameters being simultaneously indicative of the magnitude and the direction of the change in user interactions between the control and treatment version.

Abstract: Methods and systems for generating a combined metric parameter for A/B testing comprising: acquiring a respective first metric parameter for a first and second plurality of feature vectors, a combination of the respective first metric parameters being indicative of a direction of a change in user interactions between the control version and the treatment version, acquiring a respective second metric parameter for the first and second plurality of feature vectors, a combination of the respective second metric parameters being indicative of a magnitude of the change in user interactions between the control and treatment version, generating a respective combined control metric parameter for the first plurality of feature vectors and the second plurality of feature vectors, the combination of the respective combined metric parameters being simultaneously indicative of the magnitude and the direction of the change in user interactions between the control and treatment version.

Abstract: The methods and systems described herein relate to conducting a controlled experiment using prediction of future user behavior. The method, executable on at least one server, comprises: collecting behavior data on two sets of users over a first period, wherein: the first set of users is exposed to a control; the second set of users is exposed to a treatment variant; and the behavior data relates to a performance parameter of the controlled experiment; based on a prediction model applied to the behavior data, calculating predicted values of the performance parameter for each user of the first set and the second set of users over a second period of time; and determining if a difference exists between the predicted values of the performance parameter for each user of the first set of users and the predicted values of the performance parameter for each user of the second set of users.

Abstract: Methods and systems for generating a combined metric parameter for A/B testing comprising: acquiring a respective first metric parameter for a first and second plurality of feature vectors, a combination of the respective first metric parameters being indicative of a direction of a change in user interactions between the control version and the treatment version, acquiring a respective second metric parameter for the first and second plurality of feature vectors, a combination of the respective second metric parameters being indicative of a magnitude of the change in user interactions between the control and treatment version, generating a respective combined control metric parameter for the first plurality of feature vectors and the second plurality of feature vectors, the combination of the respective combined metric parameters being simultaneously indicative of the magnitude and the direction of the change in user interactions between the control and treatment version.

Abstract: Methods and systems for selecting a selected-sub-set of features from a plurality of features for training a machine learning module, the training of the machine learning module to enable classification of an electronic document to a target label, the plurality of features associated with the electronic document. In one embodiment, the method comprises analyzing a given training document to extract the plurality of features, and for a given not-yet-selected feature of the plurality of features: generating a set of relevance parameters iteratively, generating a set of redundancy parameters iteratively and determining a feature significance score based on the set of relevance parameters and the set of redundancy parameters. The method further comprises selecting a feature associated with a highest value of the feature significance score and adding the selected feature to the selected-sub-set of features.

Abstract: The methods and systems described herein relate to conducting a controlled experiment using prediction of future user behavior. The method, executable on at least one server, comprises: collecting behavior data on two sets of users over a first period, wherein: the first set of users is exposed to a control; the second set of users is exposed to a treatment variant; and the behavior data relates to a performance parameter of the controlled experiment; based on a prediction model applied to the behavior data, calculating predicted values of the performance parameter for each user of the first set and the second set of users over a second period of time; and determining if a difference exists between the predicted values of the performance parameter for each user of the first set of users and the predicted values of the performance parameter for each user of the second set of users.