Abstract: A method and system for meeting a service level of content item promotion. The method is executable by a server hosting a recommendation service for providing digital content items to a user, the user being one of a plurality of users of the service. Storage communicatively coupled to the server contains data representative of the digital content items, and of the history of interactions of the plurality of users of the service with such digital content items. The method includes upon the server receiving a request for a promotion of a digital content item with a requested service level, calculating a threshold of probability within a distribution of users based on their likelihood that they take action over the digital content item, and providing the digital content item to users within the plurality of users of the service having a probability within the distribution that is above the threshold.

Abstract: A method and system for meeting a service level of content item promotion. The method is executable by a server hosting a recommendation service for providing digital content items to a user, the user being one of a plurality of users of the service. Storage communicatively coupled to the server contains data representative of the digital content items, and of the history of interactions of the plurality of users of the service with such digital content items. The method includes upon the server receiving a request for a promotion of a digital content item with a requested service level, calculating a threshold of probability within a distribution of users based on their likelihood that they take action over the digital content item, and providing the digital content item to users within the plurality of users of the service having a probability within the distribution that is above the 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 and server for selecting recommendation items is provided. The method comprises receiving indication of user accessing a recommendation service. The method comprises determining a user-type of user being one of a new-user and old-user type. The method comprises, responsive to the user being of the new-user type: receiving information associated with a set of items from a landing page of a pre-determined resource being indicative of visual characteristics of the items; generating a feature vector for each item based on information associated with visual characteristics; generating by an MLA a user-non-specific popularity score for each item from based on the respective feature vector; generating a set of user-non-specific recommendation items by selecting from the items user-non-specific recommendation items to be presented to the user; and transmitting the set of user-non-specific recommendation items in lieu of the personalized content recommendation. A method of training the MLA is also provided.