Abstract: There is disclosed a method system for selecting a label for a task, the method comprising: receiving a plurality of labels, each of the label included within the plurality of labels being indicative of a given assessor's perceived preference of a first object of over a second object; analyzing the comparison task to determine a set of latent biasing features; executing a MLA configured to generating a respective latent score parameter for the first object and the second object, the respective latent score parameter indicative of a probable offset between the given assessor's perceived preference and an unbiased preference parameter of the first object over the second object; generating a predicted bias degree parameter for the given assessor; generating the unbiased preference parameter; using, by the server, the unbiased preference parameter as the label for the comparison task for the given assessor.

Abstract: Methods and servers for generating a modifiable portion of a digital document which has message slots for placement of digital messages are disclosed. The generating being performed during a first selection-round of a plurality of selection-rounds. The method includes, during the first selection-round: providing access to a first combination of originators that are to bid for message slots during the first selection-round. The first combination is selected by limiting (i) the number of originators that are to bid for message slots, and (ii) which combination of originators are to bid for message slots. The method includes acquiring bidding data for the first selection-round. The method includes triggering placement of digital messages in the message slots of the modifiable portion based on the bidding data, and updating data about the first combination of originators for performing a subsequent selection-round.

Abstract: There is disclosed a method and system for determining a productivity rate of a user in a computer-implemented crowd-sourced environment. The method comprises, in a training phase: acquiring a training project; determining, set of project-specific features indicative of one or more characteristics of the training project; acquiring, a plurality of training results; determining a set of user-task specific features; determining, a user activity history associated with the user; generating a set of training data including the set of project-specific features, the set of user-task specific features, and the user activity history; training a machine learning algorithm (MLA), the training including: determining, a set of features representative of a property of the set of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine the productivity rate of the user for a given project.

Abstract: A method and system for completing a task executed in a crowd-sourced environment is disclosed. The method comprises, at a training phase: transmitting a training task to a plurality of users; acquiring a plurality of training results responsive to the training task; acquiring a respective user activity history associated with each user; for each of the plurality of training results, assigning a label value; generating a set of triples of training data; and training a machine learning algorithm, the training including, determining for each triples of data a set of features representative of a property of the triples of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine a error parameter of a given result received from a given user to a given task.

Abstract: There is disclosed a method and system for presenting a digital task in a computer-implemented crowd-sourced environment. The method comprises: receiving from an electronic device an indication of a desire to complete one or more digital tasks; generating a subset of digital tasks from the plurality of digital tasks; for each of the subset of digital tasks, determining an exposure parameter; generating a ranked list of the subset of digital tasks, the generating including: generating a first ranked list of the subset of digital tasks; based on the exposure parameter of the given one of the subset of digital tasks not meeting a pre-determined threshold, promoting a rank of the given one of the subset of digital tasks to generate a final ranked list; transmitting the final ranked list to the electronic device for displaying.

Abstract: Methods and servers for generating a modifiable portion of a digital document which has message slots for placement of digital messages are disclosed. The generating being performed during a first selection-round of a plurality of selection-rounds. The method includes, during the first selection-round: providing access to a first combination of originators that are to bid for message slots during the first selection-round. The first combination is selected by limiting (i) the number of originators that are to bid for message slots, and (ii) which combination of originators are to bid for message slots. The method includes acquiring bidding data for the first selection-round. The method includes triggering placement of digital messages in the message slots of the modifiable portion based on the bidding data, and updating data about the first combination of originators for performing a subsequent selection-round.

Abstract: There is disclosed a method system for selecting a label for a task, the method comprising: receiving a plurality of labels, each of the label included within the plurality of labels being indicative of a given assessor's perceived preference of a first object of over a second object; analyzing the comparison task to determine a set of latent biasing features; executing a MLA configured to generating a respective latent score parameter for the first object and the second object, the respective latent score parameter indicative of a probable offset between the given assessor's perceived preference and an unbiased preference parameter of the first object over the second object; generating a predicted bias degree parameter for the given assessor; generating the unbiased preference parameter; using, by the server, the unbiased preference parameter as the label for the comparison task for the given assessor.

Abstract: There is disclosed a method and system for determining a productivity rate of a user in a computer-implemented crowd-sourced environment. The method comprises, in a training phase: acquiring a training project; determining, set of project-specific features indicative of one or more characteristics of the training project; acquiring, a plurality of training results; determining a set of user-task specific features; determining, a user activity history associated with the user; generating a set of training data including the set of project-specific features, the set of user-task specific features, and the user activity history; training a machine learning algorithm (MLA), the training including: determining, a set of features representative of a property of the set of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine the productivity rate of the user for a given project.

Abstract: A method and system for completing a task executed in a crowd-sourced environment is disclosed. The method comprises, at a training phase: transmitting a training task to a plurality of users; acquiring a plurality of training results responsive to the training task; acquiring a respective user activity history associated with each user; for each of the plurality of training results, assigning a label value; generating a set of triples of training data; and training a machine learning algorithm, the training including, determining for each triples of data a set of features representative of a property of the triples of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine a error parameter of a given result received from a given user to a given task.