Abstract: There is disclosed a method and a system for creating a weather forecast. The method is implemented on a server. The method comprises: receiving, by the machine learning module, at least one current weather measurement parameter at a measurement time; receiving, by the machine learning module, a first average value of a historical weather parameter for the measurement time; generating, by the machine learning module, a normalized value of the weather measurement parameter based on a difference between the current weather measurement parameter and the first average value of the historical weather parameter for the measurement time; training the machine learning module to create a normalized value of a weather forecasting parameter based on, at least partially, the normalized value of the weather measuring parameter, the normalized value of the weather forecasting parameter being associated with a future forecasting time, the future forecasting time occurring after the measurement time.

Abstract: A method of generating a recommended subset of items for a user of an electronic device is disclosed. The method is executed at a server and comprises identifying a first subset of items within a set of potentially recommendable items based on item features of items within the set of potentially recommendable items. The method also comprises acquiring a request for the recommended subset of items and identifying a second subset of items within the set of potentially recommendable items based on user events associated with the user. Each item within the second subset of items is different from any item within the first subset of items. The method also comprises generating the recommended subset of items which comprises at least some items from the first subset of items and at least some items from the second subset of items.

Abstract: There is disclosed a method for generating a content recommendation for a given user of a recommendation system. The method comprises: receiving a request for the content recommendation; responsive to the request generating a set of content recommendations for the given user, the generating being executed by a prediction module of the recommendation server, the prediction module having been trained using a training set of training events, such that for each given training event from the training set of training events: at least one user-nonspecific feature is used as a first input parameter for the prediction module training, the at least one user-nonspecific feature having been retrieved from a latest version of a snapshot archive available at a time of the given training event occurring; and at least one user-specific feature is used as a second input parameter for the prediction module training.

Abstract: A method of generating a recommended subset of items for a user of an electronic device, the method being executed at a server, the method comprises: acquiring user events associated with a plurality of users, the user events comprising indications of user queries; for each of the user queries, generating a ranked predicted items list that comprises at least some items from a set of potentially recommendable items, such that each particular item within the ranked predicted items list has an associated rank; for each item within a plurality of ranked predicted items lists, generating, by the server, an item score based on a totality of ranks associated therewith; generating the recommended subset of items from the set of potentially recommendable items by selecting at least one item within the plurality of ranked predicted items lists as the recommended subset of items based on the item scores.

Abstract: There is disclosed a method and a system for creating a weather forecast. The method is implemented on a server. The method comprises: receiving, by the machine learning module, at least one current weather measurement parameter at a measurement time; receiving, by the machine learning module, a first average value of a historical weather parameter for the measurement time; generating, by the machine learning module, a normalized value of the weather measurement parameter based on a difference between the current weather measurement parameter and the first average value of the historical weather parameter for the measurement time; training the machine learning module to create a normalized value of a weather forecasting parameter based on, at least partially, the normalized value of the weather measuring parameter, the normalized value of the weather forecasting parameter being associated with a future forecasting time, the future forecasting time occurring after the measurement time.

Abstract: A method of generating a recommended subset of items for a user of an electronic device, the method being executed at a server, the method comprises: identifying, by the server, a first subset of items within a set of potentially recommendable items based on item features of items within the set of potentially recommendable items; acquiring, by the server, a request for the recommended subset of items; identifying, by the server, a second subset of items within the set of potentially recommendable items based on user events associated with the user, each item within the second subset of items being different from any item within the first subset of items; and generating, by the server, the recommended subset of items, the recommended subset of items comprising at least some items from the first subset of items and at least some items from the second subset of items.

Abstract: A method of generating a recommended subset of items for a user of an electronic device, the method being executed at a server, the method comprises: acquiring user events associated with a plurality of users, the user events comprising indications of user queries; for each of the user queries, generating a ranked predicted items list that comprises at least some items from a set of potentially recommendable items, such that each particular item within the ranked predicted items list has an associated rank; for each item within a plurality of ranked predicted items lists, generating, by the server, an item score based on a totality of ranks associated therewith; generating the recommended subset of items from the set of potentially recommendable items by selecting at least one item within the plurality of ranked predicted items lists as the recommended subset of items based on the item scores.

Abstract: There is disclosed a method for generating a content recommendation for a given user of a recommendation system. The method comprises: receiving a request for the content recommendation; responsive to the request generating a set of content recommendations for the given user, the generating being executed by a prediction module of the recommendation server, the prediction module having been trained using a training set of training events, such that for each given training event from the training set of training events: at least one user-nonspecific feature is used as a first input parameter for the prediction module training, the at least one user-nonspecific feature having been retrieved from a latest version of a snapshot archive available at a time of the given training event occurring; and at least one user-specific feature is used as a second input parameter for the prediction module training.