Systems and methods of providing recommendations of content items
AssigneeRCRDCLUB Corporation
InventorsDamian Franken Manning, Omar Emad Shams, Samuel Evan Sandberg
Life of this patent
Estimating the term…
Abstract
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.
Claims26
A method of automatically generating item recommendations for users in a system having a processor, and a database, and connected to a user device through a network, the method comprising:
- obtaining a matrix for a plurality of users and a plurality of items, the matrix indicating whether each user has previously consumed each item;
- generating, using the processor, an n-dimensional vector representation for each user and an m-dimensional vector representation for each item, wherein each of n and m is at least 2;
- reducing, using the processor, each user vector representation and each item vector representation to a two-dimensional space;
- creating a plurality of clusters having cluster centers by using one or more first mixture models based on the reduced vector representations, the one or more first mixture models executed via the processor to create the plurality of clusters;
- using the cluster centers for one or more second mixture models to create discrete categories to which each of the plurality of items are assigned, the one or more second mixture models executing via the processor to create the discrete categories and to determine a probability of each of the plurality of items being assigned to one or more of each of the discrete categories, wherein at least one of the items is assigned to multiple categories;
- identifying an item of interest from the plurality of items assigned to the discrete categories that include at least one item previously consumed;
- and
- providing a recommendation of the item of interest to the user device via the network.
A system configured to automatically generate item recommendations for users, the system comprising:
- a memory configured to store instructions;
- a network connecting the system to a user device;
- and
- a processor configured to execute the instructions to:
- obtain a matrix for a plurality of users and a plurality of items, the matrix indicating whether each user has previously consumed each item;
- generate an n-dimensional vector representation for each user and an m-dimensional vector representation for each item, wherein each of n and m is at least 2;
- reduce each user vector representation and each item vector representation to a two-dimensional space;
- create a plurality of clusters having cluster centers by using one or more first mixture models based on the reduced vector representations;
- use the cluster centers for one or more second mixture models to create discrete categories to which each of the plurality of items are assigned;
- determine a probability, using the second one or more mixture models, of each of the plurality of items being assigned to each of the discrete categories, wherein at least one of the items is assigned to multiple categories;
- identify an item of interest from the plurality of items assigned to the discrete categories that include at least one item previously consumed;
- and
- provide a recommendation of the item of interest to the user device via the network.
Dependencies are inferred from the claim text.