Mini-GD method based on GBP dimension

A mini-gd and dimension technology, applied in the field of mini-GD based on the GBP dimension, can solve problems such as learning, large memory usage, large oscillation value, etc., achieve efficient data processing, reduce the number of training iterations, and increase the learning rate.

Inactive Publication Date: 2020-08-25
上海解兮生物科技有限公司
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a recommendation method based on the minimum gradient descent algorithm of the GBP dimension in view of the deficiencies in the above-mentioned prior art. The above-mentioned recommendation method can solve the problem that there is no way to combine the gene dimension For learning, a large amount of memory is occupied during calculation and learning. When optimizing, the oscillating value is too large. When the data is calculated in batches, the overall gradient optimization cannot be performed. This algorithm is based on the minimum gradient descent algorithm. Gene), behavior (Behavior), and phenotype (Phenotypic) are called the user's GBP data, and these data are tagged to form a user-based GBP tag; the user's GBP information is tagged Finally, the content (commodity, article, video, picture, etc.) information is tagged, and then the user's GBP tag and the content tag are matched at the algorithm level; the above algorithm is referred to as a GBP-based mini- GD algorithm, gradient optimization of data

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  • Mini-GD method based on GBP dimension

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Embodiment Construction

[0040] Combine below figure 1 The present invention will be further described with specific embodiments.

[0041]A recommended method based on the minimum gradient descent algorithm of GBP dimension, comprising the following steps:

[0042] 1) According to the user's GBP data, the user is tagged, and each user has its own GBP tag, and each tag represents a different item;

[0043] 2) Manually tag the content so that the content also has one or more tags:

[0044] 3) Preliminary matching is carried out according to the user's label and content label. After user use, feedback and algorithm calibration, the user has made a preference judgment for some items, likes some of the items, and dislikes the other part;

[0045] 4) Based on the user's past preference judgment, a general model is formed for the user, and then the label weight coefficient is adjusted according to the data feedback of the user's practice operation to optimize the recommendation mechanism of the recommendat...

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Abstract

The invention discloses a mini-GD method based on a GBP dimension. According to the method, the problem that a recommendation method in the prior art cannot be combined with gene dimensions for learning, a large amount of memory is occupied during calculation learning; an oscillation value is large in optimization, and overall gradient optimization cannot be carried out during data batch operationare solved, gradient optimization is carried out on data based on a minimum gradient descent algorithm of the GBP dimension, according to the fact that each iteration must advance towards the optimaldirection, meanwhile, no too much memory is occupied during sample training, batch training and learning are carried out, and the optimal solution is guaranteed.

Description

technical field [0001] The present invention belongs to the technical field of data mining, and in particular relates to a mini-GD method based on the GBP dimension. The above-mentioned recommendation method can solve the problem that the recommendation method in the prior art has no way to combine the gene dimension for learning, and takes up a large amount of memory during calculation and learning. When optimizing, the oscillating value is too large, and the overall gradient optimization cannot be performed when the data is calculated in batches. Background technique [0002] With the rapid development of e-commerce websites, recommendation systems have been widely studied and applied. The recommendation system obtains user preferences by extracting and analyzing user information, behaviors, ratings, etc., to help e-commerce companies find specific users for whom it is possible to recommend. Purchased products to increase sales of merchandise. [0003] The recommendation ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06Q30/06
CPCG06F16/9536G06Q30/0631
Inventor 吴健王强刘智平
Owner 上海解兮生物科技有限公司
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