Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.

Abstract: A method and a system for generating a digital content recommendation. The method comprises receiving a request for the digital content recommendation. Based on the request, a first content item and a second content item responsive to the request are selected, and a relevancy parameter and a completion parameter for each of the first content item and the second content item are determined. Based on the relevancy parameter and the completion parameter, the first content item and the second content item are ranked, and a digital content recommendation is generated based on the ranking of the first content item and the second content item.

Abstract: A method and system for determining user-specific proportions of types of content for recommendation to a given user comprising: acquiring for each respective type of content of at least two types of content, a respective base interval of proportion of content for recommendation, computing for each respective type of content of the at least two types of content an associated respective distribution of user interaction parameters associated with a respective set of users, acquiring an associated respective user-specific interaction parameter of the given user, computing for each respective type of content a respective user-specific proportion of the respective type of content for content recommendation to the given user, the respective user-specific proportion being within the respective base interval of proportion of content, the computing being based on: the respective distribution of user interaction parameters of the set of users, and the respective user-specific interaction parameter of the given user.

Abstract: A method and system for determining user-specific proportions of types of content for recommendation to a given user comprising: acquiring for each respective type of content of at least two types of content, a respective base interval of proportion of content for recommendation, computing for each respective type of content of the at least two types of content an associated respective distribution of user interaction parameters associated with a respective set of users, acquiring an associated respective user-specific interaction parameter of the given user, computing for each respective type of content a respective user-specific proportion of the respective type of content for content recommendation to the given user, the respective user-specific proportion being within the respective base interval of proportion of content, the computing being based on: the respective distribution of user interaction parameters of the set of users, and the respective user-specific interaction parameter of the given 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 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 a system for generating a digital content recommendation. The method comprises receiving a request for the digital content recommendation. Based on the request, a first content item and a second content item responsive to the request are selected, and a relevancy parameter and a completion parameter for each of the first content item and the second content item are determined. Based on the relevancy parameter and the completion parameter, the first content item and the second content item are ranked, and a digital content recommendation is generated based on the ranking of the first content item and the second content item.

Abstract: A method and system for determining user-specific proportions of types of content for recommendation to a given user comprising: acquiring for each respective type of content of at least two types of content, a respective base interval of proportion of content for recommendation, computing for each respective type of content of the at least two types of content an associated respective distribution of user interaction parameters associated with a respective set of users, acquiring an associated respective user-specific interaction parameter of the given user, computing for each respective type of content a respective user-specific proportion of the respective type of content for content recommendation to the given user, the respective user-specific proportion being within the respective base interval of proportion of content, the computing being based on: the respective distribution of user interaction parameters of the set of users, and the respective user-specific interaction parameter of the given user.

Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.

Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.

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 computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.