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公开(公告)号：US10187674B2
公开(公告)日：2019-01-22
申请号：US13916132
申请日：2013-06-12
Applicant: NETFLIX, Inc.
Inventor： Harald Steck
Abstract: Techniques are described for promoting original media titles. Given metadata tags associated with the original title and other media titles, a tag data matrix is generated and factored into two matrices, one of which includes vectors representing the media titles in a first latent space. Similarity scores are computed between a vector representing the original title and each of the other media title vectors to determine a set of media titles most similar to the original title. Then, a play data matrix is factorized, and an average of vectors representing the most similar titles in a second latent space is taken to be a vector representation of the original title in the second latent space. This representation is compared with representations of users in the second latent space to generate similarity scores, and the original title is then promoted to users associated with the highest similarity scores.
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公开(公告)号：US11551280B2
公开(公告)日：2023-01-10
申请号：US16664761
申请日：2019-10-25
Applicant: NETFLIX, INC.
Inventor： Harald Steck
Abstract: In various embodiments, a training application generates a preference prediction model based on an interaction matrix and a closed-form solution for minimizing a Lagrangian. The interaction matrix reflects interactions between users and items, and the Lagrangian is formed based on a constrained optimization problem associated with the interaction matrix. A service application generates a first application interface that is to be presented to the user. The service application computes predicted score(s) using the preference prediction model, where each predicted score predicts a preference of the user for a different item. The service application then determines a first item from the items to present to the user via an interface element included in the application interface. Subsequently, the service application causes a representation of the first item to be displayed via the interface element included in the application interface.
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公开(公告)号：US10180968B2
公开(公告)日：2019-01-15
申请号：US15044020
申请日：2016-02-15
Applicant: NETFLIX, INC.
Inventor： Harald Steck
Abstract: In one embodiment of the present invention, a training engine teaches a matrix factorization model to rank items for users based on implicit feedback data and a rank loss function. In operation, the training engine approximates a distribution of scores to corresponding ranks as an approximately Gaussian distribution. Based on this distribution, the training engine selects an activation function that smoothly maps between scores and ranks. To train the matrix factorization model, the training engine directly optimizes the rank loss function based on the activation function and implicit feedback data. By contrast, conventional training engines that optimize approximations of the rank loss function are typically less efficient and produce less accurate ranking models.
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