User preference prediction method based on session recommendation system

A recommendation system and prediction method technology, applied in the field of artificial intelligence, can solve problems such as insufficient capture of user and item relationships

Active Publication Date: 2021-02-12
NANKAI UNIV
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of insufficient capture of the relationship between users and items in the existing user preference prediction method of the conversational recommendation system, and propose a method for recommending using a modified graph neural network and attention pooling layer together

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  • User preference prediction method based on session recommendation system
  • User preference prediction method based on session recommendation system
  • User preference prediction method based on session recommendation system

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[0081] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0082] see figure 1 , this example

[0083] Based on the user preference prediction method of the session recommendation system, the specific steps of the method are as follows:

[0084] S1, read anonymous session data and perform preprocessing to obtain a session sequence training test set;

[0085] S2, constructing a directed weighted graph according to the session sequence obtained in step S1;

[0086] S3, based on the directed weighted graph, learns the vector representation of each vertex in the graph based on the graph neural network;

[0087] S4, based on the self-attention network and the pooling layer to obtain the user's long-term and short-term preferences respectively;

[0088] S5, using the attention mechanism to auto...

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Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a user preference prediction method based on a session recommendation system. The method is based on a neural network technology and is divided into two stages. Firstly, based on input session sequence data, a session graph is constructed, and a graph neural network is used for learning vector representation of each item. Secondly, respectively long-term and short-term preferences of the user are learned by using a self-attention network and a pooling network, and recommendation is performed bycombining an attention mechanism with the two parts. The finally obtained model is used for predicting the preference of the user.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a user preference prediction method based on a session recommendation system. Background technique [0002] Recommender systems are effective tools to deal with information overload and play an important role in application domains such as e-commerce, movies, and music. The recommendation problem is usually abstracted as a matrix filling / reconstruction problem. The main idea is to populate the user rating matrix with predictions of default values, and then perform collaborative filtering calculations. This abstract approach is suitable for training models with long-term user preferences. However, in many cases, user identity and past behavior may not be known, and only the history of user behavior in ongoing short-term sessions is available. To address this problem, session-based recommendation is proposed, which only relies on the order of the user...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q30/02G06Q30/06
CPCG06N3/084G06Q30/0201G06Q30/0631G06N3/044
Inventor 袁晓洁叶承卫
Owner NANKAI UNIV
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