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Method and device for processing interactive data using lstm neural network model

A neural network model and interactive data technology, applied in the field of using machine learning to process interactive data, can solve problems such as difficulty in feature expression

Active Publication Date: 2022-05-24
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the interaction event involves both parties, and the state of each participant can change dynamically. Therefore, it is very difficult to accurately express the characteristics of the interaction participants by comprehensively considering the various characteristics of the interaction participants.

Method used

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  • Method and device for processing interactive data using lstm neural network model
  • Method and device for processing interactive data using lstm neural network model
  • Method and device for processing interactive data using lstm neural network model

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Embodiment approach

[0147] According to another embodiment, the processing process in combination with the time difference may include the following steps:

[0148] After combining the node feature of the second node u(t), the time difference Δ, and the k implicit vectors corresponding to the k associated nodes, the first transformation function g and the second transformation function with the same algorithm and different parameters are input. f, respectively obtain k first transformation vectors and k second transformation vectors;

[0149] Combine the intermediate vector of the i-th associated node among the k associated nodes with the corresponding i-th first transformation vector and the i-th second transformation vector to obtain k operation results, and sum the k operation results , get the combined vector;

[0150] The node feature of the second node, together with the k implicit vectors, are respectively input into the third transformation function and the fourth transformation function...

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Abstract

The embodiments of this specification provide a method and device for processing interaction data. In this method, first obtain the dynamic interaction graph constructed according to the interaction event set, any node i in the graph points to the M associated nodes corresponding to the N associated events that the object represented by the node i participated in last time through the connection edge, Among them, objects are allowed to participate in multiple associated events at the same time, and nodes are allowed to connect to more than two associated nodes. Then, in the dynamic interaction graph, determine the current subgraph corresponding to the current node to be analyzed, and input the current subgraph into the neural network model for processing. The neural network model includes an LSTM layer, and the LSTM layer iteratively processes each node in turn according to the pointing relationship of the connection edges between each node in the current subgraph, so as to obtain the hidden vector of the current node.

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular, to a method and apparatus for processing interaction data using machine learning. Background technique [0002] In many scenarios, user interaction events need to be analyzed and processed. Interaction events are one of the basic elements of Internet events. For example, the click behavior of a user when browsing a page can be regarded as an interaction event between the user and the content block of the page, and the purchase behavior in e-commerce can be regarded as the interaction between the user and the product. The interaction events between accounts, and the transfer behavior between accounts is the interaction events between users. A series of user interaction events contain the characteristics of users' fine-grained habits and preferences, as well as the characteristics of interaction objects, which are important feature sources of machin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/049G06N3/08
Inventor 常晓夫文剑烽刘旭钦宋乐
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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