Method and device for training transfer learning model and recommendation model

A transfer learning and model technology, applied in the field of machine learning, can solve problems such as negative transfer, reduce the effect of transfer learning, and data distribution is not completely consistent, and achieve the effect of improving the effect and avoiding negative transfer.

Active Publication Date: 2021-08-06
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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Problems solved by technology

Since the data distribution of source domain samples and target domain samples may not be exactly the same, using all source domain samples for model training will cause negative transfer and reduce the effect of transfer learning

Method used

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  • Method and device for training transfer learning model and recommendation model
  • Method and device for training transfer learning model and recommendation model
  • Method and device for training transfer learning model and recommendation model

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Embodiment Construction

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, not all of them.

[0018] For ease of understanding, a brief introduction to the concepts related to transfer learning is given first.

[0019] Transfer learning refers to the use of the similarity relationship between data and domains to apply knowledge acquired in old domains to new domains to complete tasks in new domains or solve problems encountered in new domains.

[0020] In transfer learning, Domain is a basic concept, usually composed of data and the probability distribution of data, and is the subject of transfer learning.

[0021] Domain can be divided into source domain (Source Domain) and target domain (Target Domain). The source domain refers to the doma...

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Abstract

The invention discloses a method and a device for training a transfer learning model and a recommendation model. The method comprises the following steps of clustering a source domain sample and a target domain sample to obtain a clustering result, determining the weight of the source domain sample according to the clustering result, wherein the weight of the source domain sample is used for representing the similarity between the source domain sample and the target domain sample, determining similar samples of the target domain samples from the source domain samples according to the weights of the source domain samples so as to form training samples containing the similar samples and the target domain samples, and training the transfer learning model according to the training sample.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning, and in particular to a method and device for training transfer learning models and recommendation models. Background technique [0002] Transfer learning is a learning method that improves the performance of the model on the target domain by using data from the source domain. Since the data distribution of source domain samples and target domain samples may not be exactly the same, using all source domain samples for model training will cause negative transfer and reduce the effect of transfer learning. Contents of the invention [0003] In view of this, the present disclosure provides a method and device for training a transfer learning model and a recommendation model, so as to avoid the negative transfer phenomenon. [0004] In the first aspect, a method for training a transfer learning model is provided, the method comprising: clustering source domain samples and target d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/23G06F18/23213
Inventor 郇兆鑫王宇龙张晓露周俊黄启印
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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