Rcrdclub Corporation

Omar Emad Shams

Damian Franken Manning

Samuel Evan Sandberg

A method of recommending content items includes obtaining vector representations of items based on a matrix of items versus item users that indicates item use by the users, reducing each of the vector representations to a two-dimensional space, creating clusters having cluster centers using one or more mixture models based on the reduced vector representations, using the cluster centers for one or more mixture models to create discrete categories to which items can be assigned and providing one or more recommendations to a first user based on the item assignments within the discrete categories.

Systems and methods of providing recommendations of content items

In a method for evaluating a performance of a recommender system, a session of M content items consumed by a user can be identified. The session can be defined such that, for each of a first (M-1) content items in the session, a delay time between a completion of consumption of one of the first (M-1) content items and a commencement of consumption of a directly next content item in the session is less than a predetermined duration of time. Content items in a content item-base can be ranked, based on the first (M-1) content items, according to a likelihood of each content item in the content item-base to be consumed next. A session metric can be generated for each content item in the content item-base. The recommender system can be trained based on the session metric for the Mth content item.

Growing community of inventors

Jersey City, NJ, United States of America

Omar Emad Shams