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News recommendation method based on comparative learning

A recommendation method and news technology, which is applied in the field of news recommendation based on comparative learning, can solve the problem of inaccurate recommendation of the news recommendation system, and achieve the effects of improving coding ability, robustness, and strong modeling ability

Pending Publication Date: 2022-08-02
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to combine the idea of ​​contrastive learning and the task particularity of the news recommendation system to propose a news recommendation method based on contrastive learning to solve the technical problem that the existing news recommendation system is not accurate enough

Method used

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  • News recommendation method based on comparative learning
  • News recommendation method based on comparative learning
  • News recommendation method based on comparative learning

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Embodiment Construction

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0019] For news about the same event, different authors will have different descriptions. like figure 1 As shown, the two news stories are both describing the "Chinese women's curling victory over the UK", but there are differences in the text and entities of the two. Here the text is the title of the news, and the entity is the entity word extracted from the news title. Entity extraction can be done in two ways: simple text matching and named entity recognition based on deep learning. We can see that the content of the events conveyed by two news with different text descriptions is essentially the same. Therefore, the model should make the encoded vector representations of two news close to each other when encoding the text, while keeping dissimilar news far away from each other. figure 2 is an example of user interest extraction, user 1 br...

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Abstract

The invention discloses a news recommendation method based on comparative learning. The method comprises a user interest extraction step based on comparative learning; the user interest extraction step comprises the following steps: providing a user interest encoder, wherein the user interest encoder is configured to encode a news sequence browsed by a user to obtain an interest vector; encoding the news sequence browsed by the user to obtain a first interest vector; performing data enhancement on the news sequence browsed by the user, and encoding the news sequence after data enhancement to obtain a second interest vector; training the user interest encoder, and in the training process, introducing interest comparison learning loss which enables the first interest vector to be close to the second interest vector and enables the first interest vector to be far away from interest vectors of other users.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to the fields of data mining and machine learning, and in particular to a news recommendation method based on contrastive learning. Background technique [0002] Online news platforms, such as Tencent News and Toutiao, attract a large number of users to read digital news. However, with a large number of news articles emerging every day, it is unrealistic for users to find interesting news from a large number of online news articles. Therefore, personalized news recommendation based on users' interests is an important task for online news platforms, which can help users find news articles of interest to them and alleviate the problem of information overload. [0003] Recently, news recommendation has attracted attention in both industry and academia, and many methods have been proposed. Unlike general product recommendations, news articles are time-sensitive and go ou...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/906G06N20/00G06K9/62
CPCG06F16/9535G06F16/906G06N20/00G06F18/22
Inventor 郑海涛刘浩壮李明超江勇夏树涛肖喜
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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