TRAINING RANDOM WALKS OVER ABSORBING GRAPHS
First Claim
1. A system for specifying at least one learned parameter for a random walk over a graph, comprising:
- a graph generation component that creates a graph from a portion of data, the graph having two or more disparate nodes and one or more links between the nodes based at least in part on the data; and
a training component that learns at least one parameter with respect to performing a random walk on the graph, the parameter corresponds to at least one probability that affects direction of the random walk.
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Accused Products
Abstract
A random walk is performed over a graph, such as an augmented bipartite graph, relating to ownership data with respect to a plurality of users and items owned; the graph can provide social links between the users as well. Items can be recommended to users who do not own the items by randomly walking the graph starting at the user node to which the recommendation will be given. The random walk can step from user to user or from user to item; when an item is reached, the node can be absorbing such that the random walk terminates. The arrived item is recommended to the user. Parameters can also be provided to affect decisions made during the walk about which users to walk to and/or whether to walk to a user or an item.
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Citations
20 Claims
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1. A system for specifying at least one learned parameter for a random walk over a graph, comprising:
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a graph generation component that creates a graph from a portion of data, the graph having two or more disparate nodes and one or more links between the nodes based at least in part on the data; and a training component that learns at least one parameter with respect to performing a random walk on the graph, the parameter corresponds to at least one probability that affects direction of the random walk. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for recommending an item to a user in a media service platform, comprising:
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creating a augmented bipartite graph utilizing data from a platform, nodes of the graph are created and for each user and item in the data, at least one user node is linked to at least one item node according to the data; randomly walking the graph starting at a user node until an item node is reached, the decision of which node to traverse at each step is based on one or more weight parameters; and recommending the reached item node to a user corresponding to the starting user node. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A system for providing recommendations, comprising:
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means for randomly walking a graph having a plurality of nodes related to users and items owned by the users, the nodes are linked according to ownership and social connections between the users; means for providing a recommendation to a user based on the random walk starting from a node relating to the user and ending on an item node relating to an item the user does not already own; means for specifying parameters for weighing decisions of the random walk; and means for determining the specified parameters to make optimal recommendations.
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Specification