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: There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.

Abstract: A method of determining a relevancy parameter for a digital content item and a system for implementing the method. The digital content item is originated from a content channel associated with a recommendation system. The method is executable by the server. The method comprises: identifying a pool of users associated with the content channel, a given user of the pool of users being associated with the content channel. The method comprises using the pool of users to explore and predict a relevancy parameter. The relevancy parameter is then used for predicting relevancy parameter of the digital content item for a user outside of the pool of users based on the user interactions of the first user.

Abstract: A method of determining a relevancy parameter for a digital content item and a system for implementing the method. The digital content item is originated from a content channel associated with a recommendation system. The method is executable by the server. The method comprises: identifying a pool of users associated with the content channel, a given user of the pool of users being associated with the content channel. The method comprises using the pool of users to explore and predict a relevancy parameter. The relevancy parameter is then used for predicting relevancy parameter of the digital content item for a user outside of the pool of users based on the user interactions of the first user.

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: A method and server for presenting an item with potentially undesirable content to a user are disclosed. The method comprises: receiving a presentation request and user interactions; and generating a first list of items. Items are associated with respective features and web resources. Items are ranked in first list based on user-specific scores indicative of their estimated relevance to user. A given item is associated with a given rank in first list. The method also comprises: generating for items demoting scores indicative of a degree of undesirability of content originating from respective resources; generating for items adjusted scores based on user-specific and demoting scores; generating a second list where items are ranked according to adjusted scores, where the given item is associated with an adjusted rank in the second list; and triggering presentation of items from second list to user. The given item is presented at the adjusted rank.

Abstract: A method of training machine learning algorithm for selecting recommended content for a user of an electronic device is provided. The method is executable by a recommendation server accessible by the electronic device via a communication network, the recommendation server executing the machine learning algorithm, once trained. The method comprises: acquiring an indication of a plurality of user-item interactions, each user-item interaction being associated with a user and a digital item; based on the plurality of user-item interactions, generating a matrix of user-item relevance scores; factorizing the matrix of user-item relevance scores into a user matrix and an item matrix, said factorizing including: initializing the item matrix using item vectors, the item vectors having been generated such that digital items with similar content have similar item vectors, initializing the user matrix with user-vectors; iteratively optimizing of the user matrix and the item matrix; storing the optimized item matrix.

Abstract: There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.

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.