Abstract: Disclosed are a method and a system for determining a response to a digital task in a computer-implemented crowd-sourced environment. The method comprises determining if a number of the plurality of responses to the digital task received meets a pre-determined minimum answer threshold; in response to the number of the plurality of responses to the digital task meeting the pre-determined minimum answer threshold, executing: for each of the plurality of responses generating, by the server, a confidence parameter representing a probability of an associated one of the plurality of responses being correct; ranking the plurality of responses based on the confidence parameter to determine a top response being associated with a highest confidence parameter; and in response to the highest confidence parameter being above a pre-determined minimum confidence threshold, assigning a value of the top response as a label for the digital task and terminating the digital task execution.

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: A method and system for ranking digital object based on an objective characteristic associated therewith are provided. The method comprising: generating a set of digital objects based on a user request, the set of digital objects being rankable according to an objective characteristic thereof; receiving a filter request from the user, the filter request being based on a secondary characteristic of digital objects in the set of digital objects; determining object parameters for the digital objects in the set of digital objects, a given object parameter being indicative of a likelihood that an inclusion of a respective digital object in a re-ranked set of digital objects will increase a quality metric of the re-ranked set of digital objects; selecting digital objects based on object parameters; ranking digital objects based on respective values of the secondary characteristic, thereby generating the re-ranked set of digital objects.

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: Disclosed are a method and a system for determining a response to a digital task in a computer-implemented crowd-sourced environment. The method comprises determining if a number of the plurality of responses to the digital task received meets a pre-determined minimum answer threshold; in response to the number of the plurality of responses to the digital task meeting the pre-determined minimum answer threshold, executing: for each of the plurality of responses generating, by the server, a confidence parameter representing a probability of an associated one of the plurality of responses being correct; ranking the plurality of responses based on the confidence parameter to determine a top response being associated with a highest confidence parameter; and in response to the highest confidence parameter being above a pre-determined minimum confidence threshold, assigning a value of the top response as a label for the digital task and terminating the digital task execution.

Abstract: A method and system for ranking digital object based on an objective characteristic associated therewith are provided. The method comprising: generating a set of digital objects based on a user request, the set of digital objects being rankable according to an objective characteristic thereof; receiving a filter request from the user, the filter request being based on a secondary characteristic of digital objects in the set of digital objects; determining object parameters for the digital objects in the set of digital objects, a given object parameter being indicative of a likelihood that an inclusion of a respective digital object in a re-ranked set of digital objects will increase a quality metric of the re-ranked set of digital objects; selecting digital objects based on object parameters; ranking digital objects based on respective values of the secondary characteristic, thereby generating the re-ranked set of digital objects.

Abstract: A method and system is described including presenting a first SERP version to a first set of users and a second SERP version to a second set of users; assessing first and second measures of user interactions with the first and second version of the SERP respectively, the user interactions being of a pre-selected type; computing a first and a second distribution of the first and second measures of user interactions and analyzing the first and second distributions conjointly for determining a change in user interactivity. The analyzing can include determining a set of ratios; determining a lowest ratio indicative of a smallest relative change and a highest ratio being indicative of a largest relative change within the set of ratios; determining the magnitude of the change in user interactivity based on the lowest and highest ratios; and determining a significance of the magnitude of the change in user interactivity.

Abstract: There is disclosed a computer-implemented method for predicting content item popularity. The method includes receiving, from a crawler database, an indication of a content item; receiving, from logs, the logs comprising a search log and a browsing log, a search logs data and a browsing logs data, the search logs data representing search activity from one or more users of the search engine server directed to the content item, and the browsing logs data representing browsing activity from one or more users of a browser application directed to the content item; receiving, from the crawler database, a statistical web data representing at least one of embeds or links of the content item contained in one or more web resources directed to the content item; and, predicting the content popularity, based at least in part the search logs data; the browsing logs data; and the statistical web data.

Abstract: A method and system is described including presenting a first SERP version to a first set of users and a second SERP version to a second set of users; assessing first and second measures of user interactions with the first and second version of the SERP respectively, the user interactions being of a pre-selected type; computing a first and a second distribution of the first and second measures of user interactions and analyzing the first and second distributions conjointly for determining a change in user interactivity. The analyzing can include determining a set of ratios; determining a lowest ratio indicative of a smallest relative change and a highest ratio being indicative of a largest relative change within the set of ratios; determining the magnitude of the change in user interactivity based on the lowest and highest ratios; and determining a significance of the magnitude of the change in user interactivity.