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 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: 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: 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.