Online content recommending method based on deep neural network

A deep neural network and content recommendation technology, applied in biological neural network models, instruments, computing, etc., can solve problems such as low recommendation CTR click-through rate, data difference, and recommendation effect fluctuation.

Inactive Publication Date: 2016-01-27
SHENZHEN UNIV
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AI Technical Summary

Problems solved by technology

Traditional content-based recommendation algorithms mainly use description information such as item and user Tag. These information are usually added manually. Different people have different views on th

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  • Online content recommending method based on deep neural network
  • Online content recommending method based on deep neural network
  • Online content recommending method based on deep neural network

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Embodiment Construction

[0046] The present invention will be further described below in conjunction with the accompanying drawings. It should be noted that this embodiment is based on the technical solution, and provides detailed implementation and specific operation process, but the protection scope of the present invention is not limited to the present invention. Example.

[0047] Such as figure 1 Described, described online content recommendation method based on deep neural network comprises the steps:

[0048] S1 word vector model training.

[0049] The word vector model training process is as follows figure 2 shown. Before analyzing the text information of the content to be pushed, the text is first segmented and stop words are removed, and the important vocabulary of the content corpus is constructed as the input of the word vector tool. After obtaining the important vocabulary of the content corpus, keywords will be extracted from it to prepare for the construction of content vectors and ...

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Abstract

The invention discloses an online content recommending method based on a deep neural network. On the traditional basis of content recommendation, a deep neural network (DNN) word vector tool is introduced, content and users are mapped to high-dimensional vector space according to content text information to be recommended and the historical behaviors of the users, the cosine distance between vectors is calculated, and user groups interested in the recommended content are screened and filtered. Experiments in a large-scale moving content service system prove that compared with random recommendation, a Content KNN, Item CF and other algorithms, the recommending effect of the recommending strategy is remarkably improved.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to an online content recommendation method based on a deep neural network. Background technique [0002] With the continuous enrichment of online content and the rapid development of the mobile Internet, selecting appropriate content to push to interested users has become one of the important requirements of online content service providers. The main challenges faced are: 1. Effective representation of user characteristics and content characteristics; 2. Accurate requirements for personalized recommendation message push (too many invalid message push push notifications will disturb users and affect user experience); 3. Recommendation The complexity of the algorithm is moderate, and the calculation and execution of large-scale data can be performed based on the existing system. [0003] The existing technology based on the traditional recommendation algorithm lacks in...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/02
CPCG06F16/9535G06N3/02
Inventor 陈亮王娜李霞
Owner SHENZHEN UNIV
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