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Matrix and tensor combined decomposition-based car recommendation method and system

A recommendation method, automotive technology, applied in data processing applications, sales/rental transactions, instruments, etc., can solve the problem of not considering auxiliary information, etc.

Active Publication Date: 2017-05-31
SHANDONG UNIV
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Problems solved by technology

However, in the common process of tensor decomposition and completion, other auxiliary information is not considered

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  • Matrix and tensor combined decomposition-based car recommendation method and system
  • Matrix and tensor combined decomposition-based car recommendation method and system
  • Matrix and tensor combined decomposition-based car recommendation method and system

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] The present invention provides a method based on joint decomposition of matrix and tensor, and uses the supply relationship between the manufacturer and the supplier and the tree-like hierarchical relationship formed by the car model according to the vehicle series and the manufacturer to carry out constraints. Such as figure 1 shown, including the following steps:

[0070] Step1. Construct tensor X, matrix E and tree T, such as Figure 2(a)-Figure 2(c) shown;

[0071] Step1.1. Construct tensor X, the three dimensions of tensor are user-model-rating standard. The users in the tensor X must have commented on all attributes of at least one car. Similarly, the models must be rated by at least one user. There are 8 scoring criteria, namely: space, power, comfort, fuel consumption, handling, appearance, interior and cost performance. x ijk Indic...

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Abstract

The invention discloses a matrix and tensor combined decomposition-based car recommendation method and system. The method comprises the following steps of constructing a car scoring tensor, constructing a car manufacturer and supplier relationship matrix, and constructing a car product structure tree, wherein the car manufacturer and supplier relationship matrix and the car product structure are both complete and used for assisting in predicting a missing specific value in the tensor; according to the car product structure tree, introducing a tree-guided group lasso model for performing stipulation on a final loss function to obtain a weight; establishing a loss function, and performing iteration on the loss function by using an alternating least square method; performing derivation and zero setting on the loss function, and then calculating an iterative function of the matrix; recovering the tensor, namely complementing a missing value of the tensor; and for different users, based on elements in the complemented tensor X, recommending favorite car types of the users to the users according to a sequence of scores from high to low.

Description

technical field [0001] The invention relates to a car recommendation method and system based on matrix and tensor joint decomposition. Background technique [0002] The origin of the recommendation problem is to recommend new products or products that may be purchased to the user according to the existing information between the user and the product. The common method is often to predict the products that the user may like according to the connection between the user and the product, such as the user's purchase record or the user's scoring record, and then recommend it to the user. In the automotive field, it will predict the ratings of other models based on a user's rating of a certain model. And such scoring records are often extremely sparse. [0003] The tensor decomposition and completion technology considers the multi-faceted relationship between users and models, such as the scoring of users-models-evaluation indicators, which can better analyze the potential relati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 史秀涛王雅芳徐增林李广西刘士军武蕾蒋倩玉
Owner SHANDONG UNIV
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