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: 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: 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: 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: 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: 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: 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: A method and server for determining a creation time of a target page is disclosed. A page network of a plurality of pages including the target page and a plurality of links therebetween is created. Each page is a source or destination page for at least one link. Each page has a creation time and a creation time parameter. An initial value is assigned to the creation time parameter for each page. The creation time parameter of the target page is varied to maximize a page network probability which is based on a link probability for each link which is based at least in part on a difference between the creation time parameters of the source and destination pages thereof. The creation time of the target page is determined to be the value of the creation time parameter thereof which maximizes the page network probability.

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 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: 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 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.

Abstract: A method and server for determining a creation time of a target page is disclosed. A page network of a plurality of pages including the target page and a plurality of links therebetween is created. Each page is a source or destination page for at least one link. Each page has a creation time and a creation time parameter. An initial value is assigned to the creation time parameter for each page. The creation time parameter of the target page is varied to maximize a page network probability which is based on a link probability for each link which is based at least in part on a difference between the creation time parameters of the source and destination pages thereof. The creation time of the target page is determined to be the value of the creation time parameter thereof which maximizes the page network probability.

Abstract: There are provided methods and systems for presenting a personalized aggregated search results page (SERP) to a user in response to a search query. The method can be executable at a server. The method comprises appreciating a user-specific aggregation preference parameter, the user-specific aggregation preference parameter having been generated based on at least one feature of the user's search history; ranking a first general search result item and a first vertical search result item relative to each other based at least on the user-specific aggregation preference parameter, to generate a ranked order of search results items; and causing an electronic device associated with the user to display the ranked order of search results items within the SERP.