Method and server for selecting recommendation items for a user

A method of training a machine learned algorithm (MLA) to predict user-non-specific popularity scores of items for a user of a recommendation service, the recommendation service being hosted by a server, the user being of a new-user type, the new-user type being associated with users of the recommendation service that are associated with a pre-determined level of previous user interactions with the recommendation service, the pre-determined level of previous user interactions not being sufficient to generate a personalized user content recommendation, the server implementing the MLA, the method comprising:

• receiving, by the server, information associated with a set of items from a landing page of a pre-determined resource, the information associated with the set of items being indicative of at least visual characteristics of a respective one of the set of items, the landing page of the pre-determined resource comprising pre-selected items of a plurality of items hosted by the pre-determined resource;
• receiving, by the server, an indication of previous user interactions associated with each one of the set of items on the landing page;
• generating, by the server, a feature vector for each one of the set of items from the landing page based on information associated with the visual characteristics of a respective one of the set of items on the landing page;
• generating, by the server, a respective training set for each one of the set of items based on the respective feature vector and the respective user interactions;
• and
• training, by the server, an MLA based on the plurality of training sets to predict a user-non-specific popularity score of a given item, the user-non-specific popularity score being independent from any given user.

A server for selecting recommendation items to be transmitted to an electronic device associated with a user of a recommendation service, the recommendation service being hosted by the server, the server being configured to:

• receive an indication of the user accessing the recommendation service;
• determine a user-type of the user of the recommendation service, the user-type being one of a new-user type and an old-user type, the new-user type being associated with users of the recommendation service that are associated with a pre-determined level of previous user interactions with the recommendation service, the pre-determined level of previous user interactions not being sufficient to generate a personalized content recommendation;
• responsive to the user being of the new-user type:
• receive information associated with a set of items from a landing page of a pre-determined resource, the information associated with the set of items being indicative of at least visual characteristics of a respective one of the set of items, the landing page of the pre-determined resource comprising pre-selected items of a plurality of items hosted by the pre-determined resource;
• generate a feature vector for each one of the set of items from the landing page based on information associated with the visual characteristics of a respective one of the set of items on the landing page;
• generate, by employing a machine learned algorithm (MLA), a user-non-specific popularity score for each one of the set of items from the landing page based on the respective feature vectors, the MLA having been trained to generate user-non-specific popularity scores for given items based on respective feature vectors and respective user interactions with the given items;
• generate a set of user-non-specific recommendation items by selecting from the set of items user-non-specific recommendation items to be presented to the user based on the respective user-non-specific popularity scores;
• and
• transmit the set of user-non-specific recommendation items to the electronic device in lieu of the personalized content recommendation.