Session social recommendation method based on context neighborhood modeling

A neighbor relationship, recommendation method technology, applied in the field of Internet services, can solve the problem of not considering the interests of target users and friends

Active Publication Date: 2020-06-02
CHINA JILIANG UNIV
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AI Technical Summary

Problems solved by technology

[0004] But what these methods have in common is that the vector representation of the same friend remains unchanged for different target users, without considering that the interests of the target user and friends only partially overlap

Method used

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  • Session social recommendation method based on context neighborhood modeling
  • Session social recommendation method based on context neighborhood modeling
  • Session social recommendation method based on context neighborhood modeling

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Embodiment Construction

[0045] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] First, the variables and formulas used need to be defined.

[0047] Definition 1. U: set of users, and U={u 1 , u 2 ,...,u n}.

[0048] Definition 2. V: collection of items, and V={v 1 ,v 2 ,...,v m}.

[0049] Definition 3.G: A social network about users and user relationships.

[0050] Definition 4.N(i): user u in social network G i set of neighbors.

[0051] Definition 5. user u i A session at time t, where a session is a collection of items in a time period

[0052] Definition 6. S(i): user u i Session collection at all times,

[0053] Definition 7.q j : item v j vector representation of .

[0054] Definition 8.p i : user u obtained from user behavior i The interest vector representation of .

[0055] Definition 9.f (i,l)...

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Abstract

The invention discloses a session social recommendation method based on context neighborhood modeling. According to the method, based on historical interaction data and social network information of agiven target user, the next item most likely to be interacted by the target user is found out. According to the method, firstly, user interests are modeled, and then the representation of each friendin a social network corresponding to a target user is obtained by adopting a session-level attention mechanism. And the social influence of friends on the user is learned by using a social network. And finally, article recommendation is performed in combination with the social influence of friends on the target user and the interest of the target user. The method overcomes the defects in the existing method that the interests between the target user and friends are ignored and only partially coincide. Therefore, compared with an existing method, the recommendation effect implemented by the method is remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of Internet services, and in particular relates to a conversational social recommendation method based on contextual neighbor relationship modeling. Background technique [0002] Many online platforms, such as Yelp, Epinions, etc., allow users to share their interests and experiences and interact with other users on the platform. Many social recommendation methods take social influence into account when recommending items, which can reduce the sparsity of data. The starting point of the present invention is to combine the user interaction behavior data and the user's social network in a complementary manner to improve the accuracy of the personalized recommendation method. [0003] Most social recommendation methods adopt a matrix factorization model, which jointly models the user's social network and the user's interaction network. In recent years, with the development of graph convolutional networks (GCN...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06K9/62G06N3/04G06N3/08
CPCG06F16/9536G06N3/08G06N3/044G06N3/045G06F18/22G06F18/2411
Inventor 顾盼
Owner CHINA JILIANG UNIV
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