Abstract: A method and system for determining a result for a task executed in a crowd-sourced environment is disclosed. The method comprises receiving, a plurality of results of the task having been submitted by a plurality of human assessors; receiving a quality score for each human assessor of the plurality of human assessors; generating a plurality of vector representations comprising a vector representation for each of the results; mapping, the plurality of vector representations into a vector space; clustering the plurality of vector representations into at least a first cluster and a second cluster; executing a machine learning algorithm configured to generate a first confidence parameter and a second confidence parameter; in response to a given one of the first confidence parameter and the second confidence parameter meeting a predetermined condition, generating, an aggregated vector representation; and selecting the aggregated vector representation as the result of the task.

Abstract: A method of completing a task, the task being of a given type of task. The method includes receiving, by a server, an indication of a first result of the task having been completed by a human assessor, executing by the server a machine learning algorithm (MLA) to complete the task by the MLA to generate a second result of the task, determining, by the server, a confidence level parameter indicative of a probability of the first result being correct, and determining, by the server, whether the probability indicated by the determined confidence level parameter exceeds a pre-defined threshold probability. In response to determining that the probability indicated by the determined confidence level parameter exceeds the pre-defined threshold probability, the server processes the task as having been completed with the first result.

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: A method and system for determining a result for a task executed in a crowd-sourced environment is disclosed. The method comprises receiving, a plurality of results of the task having been submitted by a plurality of human assessors; receiving a quality score for each human assessor of the plurality of human assessors; generating a plurality of vector representations comprising a vector representation for each of the results; mapping, the plurality of vector representations into a vector space; clustering the plurality of vector representations into at least a first cluster and a second cluster; executing a machine learning algorithm configured to generate a first confidence parameter and a second confidence parameter; in response to a given one of the first confidence parameter and the second confidence parameter meeting a predetermined condition, generating, an aggregated vector representation; and selecting the aggregated vector representation as the result of the task.

Abstract: A method of completing a task, the task being of a given type of task. The method includes receiving, by a server, an indication of a first result of the task having been completed by a human assessor, executing by the server a machine learning algorithm (MLA) to complete the task by the MLA to generate a second result of the task, determining, by the server, a confidence level parameter indicative of a probability of the first result being correct, and determining, by the server, whether the probability indicated by the determined confidence level parameter exceeds a pre-defined threshold probability. In response to determining that the probability indicated by the determined confidence level parameter exceeds the pre-defined threshold probability, the server processes the task as having been completed with the first result.

Abstract: There are discloses methods and systems for generating a search engine results page (SERP). The method is executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device. The method comprises, as part of generating a search result list, the search result list containing a first search result and a second search result, predicting a first interest parameter for the first search result; predicting a second interest parameter for the second search result; predicting a usefulness parameter for the first search result, the predicting being at least partially based on the first interest parameter and the second interest parameter; adjusting a position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list.

Abstract: A method for determining a crawling schedule is disclosed, the method being executable at a crawling server coupled to a first web resource server and a second web resource server. The method comprises: acquiring a first new web page associated with the first web resource server; acquiring a second new web page associated with the second web resource server; determining a first crawling benefit parameter for the first new web page, the first crawling benefit parameter being based on a predicted popularity parameter and a predicted popularity decay parameter thereof; determining a second crawling benefit parameter for the second new web page, the second crawling benefit parameter being based on a predicted popularity parameter and a predicted popularity decay parameter thereof; based on the first crawling benefit parameter and the second crawling benefit parameter, determining a crawling order for the first new web page and the second new web page.

Abstract: There is disclosed a computer implemented method of generating a training object for training a machine learning algorithm (MLA). The method comprises: acquiring a digital training document to be used in the training; transmitting the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution.

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: There is disclosed a computer implemented method for processing a search query, the method executable by a search engine server, search engine server coupled to a crawled web resource database and a communication network. The computer implemented method aims at placing lower ranked web resources (for example, due to lack of prior user interaction with these web resources, as they may be new, etc) on the upper positions of the SERP to attract more user feedback to gather information about the lower ranked web resources. In this way, the search engine provider may improve the search results mix by giving a chance to get user feedback (and, hence, improve their scores) for more potentially highly relevant web resources (which may yet lack user interaction data to allow proper high scoring).

Abstract: A method and system for processing a user request for a recommended area of interest includes the steps of receiving the request including an indication of an electronic device geo-location and a user defined search constraint; receiving data associated with photographs associated with geo-objects, the data comprising geo-location coordinates of the photographs, the geo-location coordinates of the photographs being in proximity with the device geo-location; computing a plurality of region representations based on the geo-location coordinates of the photographs, each region representation being associated with a unique photograph density calculation parameter, the computing comprises determining a potential area of interest in each region representation, each region representation being a candidate for an optimal region representation; determining the optimal region representation based on the user defined search constraint; and displaying to the user the recommended area of interest that corresponds to the po

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: There is disclosed a computer implemented method of generating a training object for training a machine learning algorithm (MLA). The method comprises: acquiring a digital training document to be used in the training; transmitting the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution.

Abstract: There is disclosed a computer implemented method for training a search ranker, the search ranker being configured to ranking search results. The method comprises: retrieving, by the server, a training dataset including a plurality of training objects; for each training object, based on the corresponding associated object feature vector: determining a weight parameter, the weight parameter being indicative of a quality of the label; determining a relevance parameter, the relevance parameter being indicative of a moderated value of the labels relative to other labels within the training dataset; training the search ranker using the plurality of training objects of the training dataset, the determined relevance parameter for each training object of the plurality of training objects of the training dataset, and the determined weight parameter for each object of the plurality of training objects of the training dataset to rank a new document.

Abstract: There is disclosed a method of setting up a crawling schedule, the method executable at a crawling server, the crawling server coupled to a communication network, the communication network having coupled thereto a first web resource server and a second web 5 resource server.

Abstract: There are discloses methods and systems for generating a search engine results page (SERP). The method is executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device. The method comprises, as part of generating a search result list, the search result list containing a first search result and a second search result, predicting a first interest parameter for the first search result; predicting a second interest parameter for the second search result; predicting a usefulness parameter for the first search result, the predicting being at least partially based on the first interest parameter and the second interest parameter; adjusting a position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list.

Abstract: There is disclosed a computer implemented method for processing a search query, the method executable by a search engine server, search engine server coupled to a crawled web resource database and a communication network. The computer implemented method aims at placing lower ranked web resources (for example, due to lack of prior user interaction with these web resources, as they may be new, etc) on the upper positions of the SERP to attract more user feedback to gather information about the lower ranked web resources. In this way, the search engine provider may improve the search results mix by giving a chance to get user feedback (and, hence, improve their scores) for more potentially highly relevant web resources (which may yet lack user interaction data to allow proper high scoring).

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: A method and system for processing a user request for a recommended area of interest includes the steps of receiving the request including an indication of an electronic device geo-location and a user defined search constraint; receiving data associated with photographs associated with geo-objects, the data comprising geo-location coordinates of the photographs, the geo-location coordinates of the photographs being in proximity with the device geo-location; computing a plurality of region representations based on the geo-location coordinates of the photographs, each region representation being associated with a unique photograph density calculation parameter, the computing comprises determining a potential area of interest in each region representation, each region representation being a candidate for an optimal region representation; determining the optimal region representation based on the user defined search constraint; and displaying to the user the recommended area of interest that corresponds to the po

Abstract: A computerized method for optimizing search result rankings obtained from a search result ranker has the steps of retrieving a first set of query-document pairs, each query-document pair of the first set having an associated post-impression features vector; generating a weight vector having a number of weights corresponding to a number of post-impression features in each of the post-impression feature vector of the first set; generating a target function by using the weight vector and the post-impression features vectors of the first set; using a performance metric associated with the target function, optimizing the weights of the weight vector using the first set of query-document pairs to obtain an optimized target function; optimizing the search result ranker using the optimized target function; and using the optimized search result ranker to rank search results.