Training random walks over absorbing graphs
First Claim
1. A system for specifying at least one parameter for a random walk over a graph, comprising:
- a processor;
a memory communicatively coupled to the processor, the memory comprising components including;
a graph generation component that creates a graph from a portion of input data, the graph having a plurality of disparate nodes and two or more links between the nodes based at least in part on the data;
a training component that learns the 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, the training component further comprising;
a link removal component to temporarily remove at least one link of the two or more links to define a temporarily removed link; and
a link prediction component to adjust the at least one parameter to predict the temporarily removed link.
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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.
12 Citations
20 Claims
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1. A system for specifying at least one parameter for a random walk over a graph, comprising:
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a processor; a memory communicatively coupled to the processor, the memory comprising components including; a graph generation component that creates a graph from a portion of input data, the graph having a plurality of disparate nodes and two or more links between the nodes based at least in part on the data; a training component that learns the 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, the training component further comprising; a link removal component to temporarily remove at least one link of the two or more links to define a temporarily removed link; and a link prediction component to adjust the at least one parameter to predict the temporarily removed link. - 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:
under control of one or more processors executing computer-executable instructions; creating an augmented bipartite graph utilizing data from a platform, with nodes of the graph being created 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; recommending the reached item node to a user corresponding to the starting user node; and determining the one or more weight parameters in conjunction with a cost function such that a hit time is minimized while an absorption probability is maximized. - 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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a processor; a memory communicatively coupled to the processor, the memory comprising components including; a recommendation component for providing a recommendation to a user based on a random walk of a graph having a plurality of nodes related to a plurality of users and a plurality of items owned by the plurality of users, the nodes having a plurality of links therebetween according to ownership and social connections between the plurality of users, the recommendation starting from a node relating to the user and ending on an item node relating to an item the user does not already own; and a training component for specifying parameters for weighing decisions of the random walk and for determining the specified parameters to make optimal recommendations, the training component further comprising; a link removal component to temporarily remove at least one link of the plurality of links to define a temporarily removed link; and a link prediction component to adjust the at least one parameter to predict the temporarily removed link.
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Specification