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Object recommendation method and object recommendation device

A recommendation method and object technology, applied in the direction of instruments, calculations, electrical digital data processing, etc., can solve problems such as incompatibility of retraining dynamic data sources, frequent updates of user attraction matrix, unexplainable recommendation results, etc.

Active Publication Date: 2021-03-12
CANON KK
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  • Claims
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AI Technical Summary

Problems solved by technology

The retraining process is a highly time-consuming operation, so the methods proposed in the prior art literature are not feasible for recommendation applications that frequently update the user-object matrix
Moreover, the recommendation results are not interpretable, and it is difficult to explain the meaning of each element of the user factor matrix and object factor matrix in this method
[0007] For example, in a shooting spot recommendation application, the shooting spot recommendation is highly correlated with the location of the user and the spot, which often leads to data sparsity and the user spot matrix is ​​frequently updated in practical applications
Using the proposed method in this literature requires frequent retraining and is therefore incompatible with such dynamic data sources

Method used

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  • Object recommendation method and object recommendation device
  • Object recommendation method and object recommendation device
  • Object recommendation method and object recommendation device

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Embodiment Construction

[0022] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. In the interest of clarity and conciseness, not all features of the embodiments are described in the specification. It should be understood, however, that many implementation-specific settings must be made in implementing an embodiment in order to achieve the developer's specific goals, such as those constraints associated with the device and business, and that these constraints May vary from implementation to implementation. Moreover, it should also be understood that development work, while potentially complex and time-consuming, would at least be a routine undertaking for those skilled in the art having the benefit of this disclosure.

[0023] Here, it should also be noted that in order to avoid obscuring the present application with unnecessary details, only the processing steps and / or device structures closely related to at least the solution accor...

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Abstract

The present application relates to an object recommendation method and an object recommendation device. The object recommendation method includes: a feature vector extraction step, extracting at least one user feature vector representing the corresponding user feature and an object representing the corresponding object feature according to the historical data of the user and the object feature vectors, each of the user feature vectors is updated independently of each other and each of the object feature vectors is updated independently of each other in the case of historical data update; a weight matrix generating step using historical data to generate a weight matrix representing a mapping of user features to object features; and a recommendation score determining step of determining scores for a plurality of candidate objects for the selected user based on the user feature vector, the object feature vector, and the weight matrix.

Description

technical field [0001] The present application generally relates to an object recommendation method and an object recommendation apparatus for recommending objects to users based on historical data. Background technique [0002] Many object recommendation services for users, such as book, music, movie, and news recommendations, are provided on the Internet. Recommendations are made based on the collected historical data to learn the characteristics of users and objects. Typically, a collection of historical data can be represented in the form of a matrix of user objects (e.g., figure 1 ). The matrix has one dimension for a list of users and one dimension for a list of objects, with each element being recorded in relation to a user and an object. The content of the record can be the user's rating of the object (such as 0-5 points), or a digital access record, such as, 1: like; 0: dislike; or, 1: purchased; 0: not purchased ,etc. User features can be learned from a row co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/435G06F16/9535
CPCG06F16/435G06F16/9535
Inventor 王萌刘贺飞
Owner CANON KK
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