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Cross-site cold-start recommendation method based on deep learning

A technology of deep learning and recommendation method, which is applied in the direction of equipment, sales/lease transactions, commerce, etc. It can solve problems such as poor effect, limited user characteristics, and difficulty in understanding user interests, and achieve the effect of improving efficiency

Inactive Publication Date: 2017-08-29
RENMIN UNIVERSITY OF CHINA
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AI Technical Summary

Problems solved by technology

But the algorithm doesn't work very well when recommending new users
This is because the user characteristics obtained by content-based algorithms are relatively limited, and it is difficult for the system to understand the user's interests

Method used

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  • Cross-site cold-start recommendation method based on deep learning
  • Cross-site cold-start recommendation method based on deep learning
  • Cross-site cold-start recommendation method based on deep learning

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Embodiment Construction

[0008] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0009] The cross-site cold-start recommendation method proposed by the present invention involves technologies such as intelligent analysis and deep learning, and uses the information of new users of e-commerce websites on social networking sites to complete product recommendations for new users without requiring user history records. Only the user's social information is used to convert the dat...

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Abstract

The invention provides a cross-site cold-start recommendation method based on deep learning. The method achieves the recommendation of a product for a new user through the information of the new user of an e-commerce website in a social network without the historical record of the user, and can obtain the interest point of the user in a detailed manner through the social information of the user. The method comprises the steps: constructing a depth ordering model based on the user's preference; completing the conversion of the e-commerce website user information and the social network user information through the constructed model, searching the purchasing behavior similar to the habitual purchase behavior of the user in the super-user node information, and completing the recommendation, thereby improving the recommendation efficiency.

Description

technical field [0001] The invention relates to a cold start recommendation method, in particular to a deep learning-based cross-site cold start recommendation method. Background technique [0002] Social networking sites provide convenience to people's lives. Today's social networking sites have more than just the functions of mail delivery and friend contact. They are closer to people's real life. People can complete activities such as restaurant reservations, job hunting, and product purchases on social networking sites. A new feature added to the Facebook and Twitter platforms in 2014 clearly illustrates this problem, allowing users to buy products they see advertised directly by clicking a "buy" button. And many e-commerce websites allow users to use the login information of social networks (such as Facebook, Twitter and Google) to complete the login. Binding the user information of e-commerce sites and social networking sites can help to obtain richer user informati...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q30/06G06Q50/00
CPCG06Q30/0255G06Q30/0631G06Q50/01
Inventor 赵鑫黄瑾文继荣
Owner RENMIN UNIVERSITY OF CHINA
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