Abstract: There is disclosed a method and system for receiving a label for a digital task executed within a computer-implemented crowd-sourced environment, the method comprising: receiving, an indication of the digital task to be processed in the computer-implemented crowd-sourced environment; generating, a subset of examples, the subset of examples based on past digital tasks executed in the computer-implemented crowd-sourced environment, each of the subset of examples being similar to the digital task within a pre-determined similarity threshold; the subset of examples having a number of examples selected such that to provide maximum benchmark coverage with a minimum number of samples in the subset of examples; associating, the subset of examples to the digital task to be presented; causing the digital task to be presented on a computing device of at least one crowd-sourced worker in the computer-implemented crowd-sourced environment to solicit the label for the digital task.

Abstract: There is disclosed a method and system for ranking a web resource. The method comprises: receiving a set of features associated with the web resource; generating a raw quality score based on the set of features, comparing the raw quality score to a reference score, the reference score being associated with a similar level of user traffic as the web resource; applying an offset adjustment formula to the raw quality score in response to an offset between the raw quality score and the reference score to obtain an adjusted quality score; in response to receiving a query, generating a search engine result page (SERP) including an indication of the web resource at a ranked position based on the adjusted quality score, the adjusted quality score causing a future raw quality score to be closer to a future reference score associated with a future level of user traffic.

Abstract: There is disclosed a method and system for ranking a web resource. The method comprises: receiving a set of features associated with the web resource; generating a raw quality score based on the set of features, comparing the raw quality score to a reference score, the reference score being associated with a similar level of user traffic as the web resource; applying an offset adjustment formula to the raw quality score in response to an offset between the raw quality score and the reference score to obtain an adjusted quality score; in response to receiving a query, generating a search engine result page (SERP) including an indication of the web resource at a ranked position based on the adjusted quality score, the adjusted quality score causing a future raw quality score to be closer to a future reference score associated with a future level of user traffic.

Abstract: There is disclosed a method and system for receiving a label for a digital task executed within a computer-implemented crowd-sourced environment, the method comprising: receiving, an indication of the digital task to be processed in the computer-implemented crowd-sourced environment; generating, a subset of examples, the subset of examples based on past digital tasks executed in the computer-implemented crowd-sourced environment, each of the subset of examples being similar to the digital task within a pre-determined similarity threshold; the subset of examples having a number of examples selected such that to provide maximum benchmark coverage with a minimum number of samples in the subset of examples; associating, the subset of examples to the digital task to be presented; causing the digital task to be presented on a computing device of at least one crowd-sourced worker in the computer-implemented crowd-sourced environment to solicit the label for the digital task.